Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. In a dynamic environment-in terms of node mobility as well as in terms of steadily changing device parameters-the clusterhead election process has to be re-invoked according to a suitable update policy. Cluster re-organization causes additional message exchanges and computational complexity and it execution has to be optimized. Our investigations focus on the problem of minimizing clusterhead re-elections by considering stability criteria. These criteria are based on topological characteristics as well as on device parameters. This paper presents a weighted clustering algorithm optimized to avoid needless clusterhead re-elections for stable clusters in mobile ad-hoc networks. The proposed localized algorithm deals with mobility, but does not require geographical, speed or distances information.
Multi-hop ad-hoc networks are composed of a collection of devices that communicate with each other over a wireless medium [1]. Such a network can be formed spontaneously whenever devices are in transmission range. Joining and leaving of nodes occurs dynamically, particularly when dealing with mobility in ad-hoc networks. Potential applications of such networks can be found in traffic scenarios, environmental observations, ubiquitous Internet access, and in search and rescue scenarios as described in detail in [2].
Ad-hoc networks emphasize flexibility and survivability of the whole system. However, centralized approaches e.g. for group management and information provisioning do not work well in such settings. Moreover, due to frequent topology changes, connectivity of devices cannot be generally guaranteed. In particular, this makes it hard to disseminate information in a reliable way.
We overcome these limitations inherent to pure adhoc networks by (a) establishing local groups of communicating devices in a self-organizing manner and (b) introducing dedicated uplinks to a backbone infrastructure. Such uplinks are used for accessing resources available in the Internet. Additionally, they are employed to directly interconnect distant devices, either within a single partition as well as across different partitions. In practice, uplinks are realized for instance using cellular networks, satellites, or via Wi-Fi hotspots [1]. Hence, ad-hoc networks with devices that provide uplinks are called hybrid wireless networks throughout this paper. Note that uplinks normally imply additional costs and obey lower bandwidth, so that the uplink has to be applied cautiously.
Clustering Algorithm) is introduced that deals with hybrid wireless networks. WACA fosters efficient information dissemination within the ad-hoc neighborhood as well as limits the use of uplinks to the backbone network. The simulation studies conducted in [3] are based on static network topologies.
The contribution of this paper is to study WACA’s performance with respect to dynamic environments. As result of this study, we propose the introduction of a socalled king bonus mechanism in order to optimize the clusterhead election process by stabilizing efficient clusterheads.
The remainder of this paper is organized as follows. Section II describes related work. Section III introduces the mobile hybrid network models. Section IV contains a detailed description of WACA including the king bonus mechanism. The simulation studies conducted are discussed in Section V. The paper finishes with a conclusion in Section VI.
ones. Probably the most crucial point when dealing with clustering is the criterion how to choose the clusterhead. The number of clusterheads strongly influences the communication overhead, latency, inter-and intra-cluster communication design as well as the update policy (i.e. execution of re-organization of clusters).
One of the first and most influential cluster-based protocols is LEACH [4]. It uses a distributed probabilistic approach. Each node elects itself as a clusterhead with a certain probability based on the desired percentage of the clusterheads in the network, and the last round where it served as a clusterhead. Thus, the role of the clusterhead is probabilistically rotated, which enables to save a large amount of energy.
In [5], a centralized clusterhead election algorithm is presented, where the base station assigns the clusterhead roles based on the energy level and the geographical position of the nodes.
In [6], a centralized algorithm based on fuzzy logic is proposed. The nodes are selected as clusterheads by the base station based on their distances to each other, energy level, and the concentration of the nodes in the region.
Chatterjee et al. [7] propose a distributed deterministic clusterhead selection algorithm, namely WCA (Weighted Clustering Algorithm). For reasons that the proposed WACA clustering algorithm is compared to WCA in this paper, WCA is described in more detail here.
WCA obtains 1-hop clusters with one clusterhead. The election of the clusterhead is based on the weight of each node. For this a heuristic weight function is used that uses distances between the neighbors, degree (number of neighbors), speed of neighboring nodes, and battery power of the node as well as weighting factors to calculate the weight. To obtain this information, WCA assumes to be provided with geographical information or relative distances of one node and its surrounding. The WCA update policy is triggered to be invoked by isolated nodes on demand. Special cases are detachment of current clusterhead and attachment to a new clusterhead. The clusterhead continuously sends a message to its neighbors. The neighbors check if the signal strength decreases what implies that the distance to the clusterhead is increasing. In that case, the node informs its current clusterhead that it detaches and chooses the next available clusterhea
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