Cooperative communication (CC) is used in topology control as it can reduce the transmission power and expand the transmission range. However, all previous research on topology control under the CC model focused on maintaining network connectivity and minimizing the total energy consumption, which would lead to low network capacity, transmission interruption, or even network paralysis. Meanwhile, without considering the balance of energy consumption in the network, it would reduce the network lifetime and greatly affect the network performance. This paper tries to solve the above problems existing in the research on topology control under the CC model by proposing a power assignment (DCCPA) algorithm based on dynamic cooperative clustering in cooperative ad hoc networks. The new algorithm clusters the network to maximize network capacity and makes the clusters communicate with each other by CC. To reduce the number of redundant links between clusters, we design a static clustering method by using Kruskal algorithm. To maximize the network lifetime, we also propose a cluster head rotating method which can reach a good tradeoff between residual energy and distance for the cluster head reselection. Experimental results show that DCCPA can improve 80% network capacity with Cooperative Bridges algorithm; meanwhile, it can improve 20% network lifetime.

Wireless ad hoc networks consist of wireless nodes that can communicate with one another in the absence of a fixed infrastructure. There are three important issues in wireless ad hoc networks. The first is network connectivity. The network is connected if any two nodes can communicate with each other in a network. It is much more difficult to ensure that two nodes connect in a wireless network than in a wired network because of the instable wireless channel, signal attenuation, and so on. Network connectivity is closely related to the node transmission power. The greater the node transmission power, the better the network connectivity [

Topology control is one of the most important techniques used in wireless ad hoc networks to reduce energy consumption or increase network capacity on the premise of maintaining connectivity by adjusting the transmission power [

Cooperative communication is a new wireless communication technology which allows single-antenna terminal devices to share their physical resources to communicate in a multiuser environment. It forms a virtual antenna array by utilizing the broadcast feature of a signal [

Interference in a sparse network (resulting topology graph in Cooperative Bridges).

Apart from ignoring the network capacity efficiency, previous research on topology control under the CC model failed to equalize the energy consumption among network nodes. Assume that the maximal transmission power of each node is

The average node energy consumption distribution.

The distribution of the remaining energy of nodes when the first node dies.

Therefore, the paper attempts to study the problem of how to maximize network capacity and energy efficiency in cooperative ad hoc networks. We design a topology control algorithm, called DCCPA, based on a dynamic cooperative cluster algorithm and power assignment to maximize its network capacity and network lifetime.

The paper is organized as follows. We summarize some related work in Section

In this section, we focus on related works about topology control problems in cooperative ad hoc networks.

The main goal of designing cooperative ad hoc networks is to increase network capacity, reduce energy consumption, and enhance network coverage. What deserves our attention is that there is a tradeoff between the three. The way of choosing relay nodes is decided by the needs of different systems which choose different optimized objects for optimization. It is a hot issue to determine the number of relay nodes in a relay node selection algorithm.

As topology control is a network layer issue, researchers usually consider simple physical characteristics instead of variations of channel state when CC is applied in topology control. In [

Such problem has been studied in traditional topology control algorithm (without CC) [

In this section, we describe a cooperative communication model and a network model for our topology control mechanism. In addition, we also introduce a topology control problem for network capacity and energy efficiency in cooperative ad hoc networks.

The simple explanation of the cooperative communication is given by a three-node example as shown in Figure

Simple CC model.

There are two CC models in the paper. As Figure

The second CC model.

Assume that each node has a maximum transmission power

When the first CC model is put to use, the transmission power of source node

When the second CC model is applied, if

We consider a wireless ad hoc network with

In this paper, we assume that a source node can choose one or more relay nodes to transmit message. Helper node set, helper link, node connectivity, and network connectivity’s definitions are the same as [

It is the maximum amount of messages that node

A closer look at expression (

It is the minimum channel capacity on the path

It is the minimum path capacity of all paths that

It is the time which takes the first node to run out of its battery charge.

Now we can define the new topology control problem, network capacity and energy efficient topology control in cooperative ad hoc networks (CEETC). Given a 2-dimensional directed graph

As TCC problem [

In this section, we propose a dynamic cooperative clustering based power assignment (DCCPA) algorithm to solve the problem mentioned above. To keep the proposed algorithms simple and efficient, we only consider its one-hop neighbors as possible helper nodes for each node when CC is used [

This algorithm is divided into two steps: the first step is static clustering, including the intercluster communication and the transmission power assignment; the second step is cluster head rotating, including the cluster head election, the communication between cluster heads, and the transmission power reallocation. Before describing the algorithm, the pretreatment is conducted.

We denote the wireless multihop network as an undirected simple graph

Disconnected networks.

Because of the characteristics of helper nodes and cluster heads, it is relatively complex to assign power to them. For helper nodes, also cluster members, they should be able to help when they communicate with cluster heads they belong to. The power of each helper node

For the cluster head node, it not only should be able to communicate with each cluster member but also can communicate with its neighboring cluster heads; therefore the energy value assigned to cluster heads should be the maximum among these values. The power of each cluster head

The final topology structure is shown in Figure

Clustering in step 2.

Redundant CC links among clusters in step 3.

Minimum spanning tree regarding clusters as nodes.

Final topology in step 4.

Final topology in Cooperative Bridges.

Here,

As for the help node

Here,

As for the new cluster head node

Here,

According to the simulation parameters, we do two groups experiments. The simulation parameters of the first group experiment are network capacity and average transmission power. The experimental environment is the same as [

The dynamic topology evolvement by DCCPA is shown in Figure

Topology evolvement by DCCPA.

Network capacity.

Average transmission power.

From Figure

Network lifetime.

In this paper, we propose a dynamic cooperative clustering based power assignment (DCCPA) algorithm to solve a new topology control problem: network capacity and energy efficient in cooperative wireless ad hoc networks. To the best of our knowledge, this is the first study to investigate our proposed algorithm for solving the problem. We give a detailed description of the algorithm which aims to maximize the network capacity and network lifetime in cooperative wireless ad hoc networks. Simulation results demonstrate the high performance of DCCPA which can improve 80% network capacity and prolong 20% network lifetime compared with the Cooperative Bridges algorithm.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This work was partly supported by the National Natural Science Foundations of China (nos. 61272061 and 61301148), the Fundamental Research Funds for the Central Universities of China (nos. 531107040263 and 531107040276), the Research Funds for the Doctoral Program of Higher Education of China (nos. 20120161120019 and 20130161110002), the Hunan Natural Science Foundations of China (nos. 10JJ5069 and 14JJ7023), and the Open Fund Project of Key Laboratory in Hunan Universities (no. 11K017).