Lowest-ID Algorithm Research Articles

ECCT: An Efficient-Cooperative ADHOC MAC for Cluster-Based TDMA System in VANETs

In VANETs, a clustering algorithm can be used to partition the network into smaller segments. Therefore, the clustering algorithms provide not only limited channel contention but also fair channel access within the cluster. In addition, they also increase spatial reuse in VANETs and efficient control for the network topology. The main concern in the clustering algorithm is the cluster stability. The major challenge is to elect a cluster head and to maintain the membership of member nodes in a highly dynamic and a fast-changing topology. Therefore, the major criterion for cluster formation is to form a stable cluster. In this paper, a clustering algorithm to affect organization and cooperative ADHOC MAC for cluster-based TDMA system in VANETs (ECCT) is proposed. The cooperation is used to help a cluster head update each neighbor cluster's information which helps each cluster head to avoid collision when two clusters move into each other's cluster's transmission range. This efficiently utilizes a time slot. We use the lowest-ID algorithm to achieve organization. The analytical and simulated results show that not only the average number of time slots for electing a cluster head but also total number of time slots before data can be successfully transmitted is less than the existing cluster-based TDMA system and IEEE 802.11p.

Adaptive Energy-aware Cluster Based Routing Protocol for Mobile Ad Hoc Networks

Due to the downside characteristics of Mobile Ad hoc Networks (MANETs) such as dynamic topology and energy consumption and control overhead, network clustering is one of the promising solutions. Cluster Based Routing Protocol (CBRP) is a robust and scalable routing protocol for MANETs. Clustering formation algorithm used in CBRP is a variation of simple lowest-ID algorithm in which the node with a lowest ID among its neighbors is elected as the Cluster head. Neglecting mobility and energy for selecting cluster head is one of the weakness points of the algorithm. In order to increase stability of the network and to prevent re-clustering an adaptive energy-aware Cluster Based Routing Protocol (AECBRP) is proposed. Two algorithms have been introduced in AECBRP as enhancement to the CBRP: improving the cluster formation algorithm by considering relative mobility, residual energy and connectivity degree metrics, and add in an efficient cluster maintenance algorithm based on the aggregate energy metric of cluster head. Using NS-2 we evaluate the rate of cluster-head changes, the normalization routing overhead and the packet delivery ratio. Comparisons denote that the proposed AECBRP has better performances with respect to the original CBRP and Cross-CBRP.

A Position Sensitive Clustering Algorithm for VANET

In order to overcome the defects such as too many cluster heads and large overhead in traditional single hop VANET clustering algorithms when there are high density vehicles, this article proposes a multi-hop cluster algorithm named the position sensitive clustering algorithm (PSCA). Based on the establishment of cluster structure, initial cluster head is selected following the â??Randomly Competition, First Declare Winâ?? principle. Cluster members transmit their expected status messages through the maintained multiple hop routings. After a series of information fusion and calculation work, the cluster head broadcasts the management messages based on dynamic timestamps. This article also gives the cluster maintaining strategy when the cluster head disappears or new nodes join. Tests performed in the simulation by NS-2 show that the proposed algorithm has less cluster heads and lower control overhead compared to the single hop-based approach (the Lowest-ID algorithm).

Density Based Clustering algorithm for Vehicular Ad-Hoc Networks

In this paper we present a new multilevel clustering algorithm for Vehicular Ad-Hoc Networks (VANET), which we call the Density Based Clustering (DBC). Our solution is focused on the formation of stable, long living clusters. Cluster formation is based on a complex clustering metric which takes into account the density of the connection graph, the link quality and the road traffic conditions. The tests performed in the simulation environment composed of VanetMobiSim and Java in Simulation Time (JiST)/SWANS have shown that DBC performs better than the popular approach (the Lowest Id algorithm) – the clusters stability has been significantly increased.

Highly stable and adaptive clustering with efficient routing in wireless ad hoc networks

The association and disassociation of mobile nodes to and from the clusters in wireless ad hoc networks disturb the stability of the network and, therefore, the reconfiguration of clusterheads is unavoidable. This paper proposes a Weight Based Adaptive Clustering Algorithm (WBACA) that leads to a high degree of stability in the network. Through simulations we have compared the performance of WBACA with that of the Lowest-ID algorithm and the Weighted Clustering Algorithm (WCA) in terms of the number of clusters formed, number of clusterhead changes, number of reaffiliations, control overheads, throughput, and average packet transfer delay in the network.

A Framework for Evaluating the Performance of Cluster Algorithms for Hierarchical Networks

Table-driven routing algorithms in flat networks have the scalability problem due to the need for global topology updates. To reduce update cost, networks are hierarchically organized. Clustering algorithms organize flat networks into hierarchical networks. One important problem, which has not been adequately addressed so far, is to evaluate how good a clustering algorithm is. In other words, it is useful to know what the desired properties of hierarchical networks are. In this paper, we address this issue by considering the routing update cost, which can be measured by the total routing table size and the variance of cluster size distribution. We provide a set of desired properties of clustering algorithms. Applying these properties to the cluster structure generated by an algorithm, we can determine how good a clustering algorithm is. Specifically, we discuss how to choose appropriate number of hierarchy levels, number of clusters, and cluster size distribution, such that the topology update cost is minimized. The desired properties obtained from the analysis can be used as guidelines in the design of clustering algorithms for table-driven hierarchical networks. We apply the idea developed in this paper to evaluate three routing algorithms, namely the lowest ID algorithm, the maximum degree algorithm, and the variable degree clustering algorithm. We show how the variable degree clustering algorithm, which takes into account these desired properties, improves routing performance.

Size‐restricted cluster formation and cluster maintenance technique for mobile ad hoc networks

AbstractMobile ad hoc networks may be composed of a large number of nodes and hence a hierarchical cluster‐based structure can be employed to address the scalability issues of the large network. In this paper we propose a size‐restricted, distributed clustering strategy (cluster formation and cluster maintenance) for mobile ad hoc networks. A distributed approach where every node is responsible for the clustering decisions would avoid single‐point bottleneck failures. We use a size restriction S, for the cluster formation and cluster maintenance. In addition, while forming the cluster we also use a diameter restriction K. The simulations show that our strategy gives rise to a lesser number of clusters when compared to other clustering algorithms proposed by Gerla et al., Fernadess et al. and Lin et al. This is attributed to the size restriction and cluster merging that we have incorporated. The size restriction helps in better management of resources inside the cluster. Our clustering technique uses a weight‐based cluster head election strategy which results in less change in cluster head and higher cluster head lifetime when compared to the Least Cluster Head Changes (LCC) with lowest ID algorithm. The low change in cluster head and higher cluster head lifetime make our proposed algorithm suitable for high‐mobility situations. The proposed clustering strategy could be used for building the clusters required in a hierarchical structure of the ad hoc network. Copyright © 2007 John Wiley & Sons, Ltd.

SLID: A secure lowest-ID clustering algorithm

In order to solve security problem of clustering algorithm, we proposed a method to enhance the security of the well-known lowest-ID clustering algorithm. This method is based on the idea of the secret sharing and the (k, n) threshold cryptography. Each node, whether clusterhead or ordinary member, holds a share of the global certificate, and anyk nodes can communicate securely. There is no need for any clusterhead to execute extra functions more than routing. Our scheme needs some prior configuration before deployment, and can be used in critical environment with small scale. The security-enhancement for Lowest-ID algorithm can also be applied into other clustering approaches with minor modification. The feasibility of this method was verified by the simulation results.

Approximation Algorithms for the Mobile Piercing Set Problem with Applications to Clustering in Ad-Hoc Networks

The main contributions of this paper are two-fold. First, we present a simple, general framework for obtaining efficient constant-factor approximation algorithms for the mobile piercing set (MPS) problem on unit-disks for standard metrics in fixed dimension vector spaces. More specifically, we provide low constant approximations for L1 and L∞ norms on a d-dimensional space, for any fixed d > 0, and for the L2 norm on two- and three-dimensional spaces. Our framework provides a family of fully-distributed and decentralized algorithms, which adapt (asymptotically) optimally to the mobility of disks, at the expense of a low degradation on the best known approximation factors of the respective centralized algorithms: Our algorithms take O(1) time to update the piercing set maintained, per movement of a disk. We also present a family of fully-distributed algorithms for the MPS problem which either match or improve the best known approximation bounds of centralized algorithms for the respective norms and space dimensions.Second, we show how the proposed algorithms can be directly applied to provide theoretical performance analyses for two popular 1-hop clustering algorithms in ad-hoc networks: the lowest-id algorithm and the Least Cluster Change (LCC) algorithm. More specifically, we formally prove that the LCC algorithm adapts in constant time to the mobility of the network nodes, and minimizes (up to low constant factors) the number of 1-hop clusters maintained. While there is a vast literature on simulation results for the LCC and the lowest-id algorithms, these had not been formally analyzed prior to this work.We also present an O(log n)-approximation algorithm for the mobile piercing set problem for nonuniform disks (i.e., disks that may have different radii), with constant update time.