Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation

Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters

Multi-mode Tracking of a Group of Mobile Agents

We consider the problem of tracking a group of mobile nodes with limited available computational and energy resources given noisy RSSI measurements and position estimates from group members. The multilateration solutions are known for energy efficiency. However, these solutions are not directly applicable to dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. Existing algorithms such as cluster-based GPS duty-cycling, individual-based tracking, and multilateration-based tracking can only partially deal with the challenges of dynamic grouping scenarios. To cope with these challenges in an effective manner, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the topological structure of the group as well as the availability of the resources into consideration and decides the best solution at any particular time instance. We consider a clustering approach where a cluster head coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes generated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to $20\%$ in comparison to the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using almost half the energy.Comment: Accepted for publication in the 20th international symposium on wireless personal multimedia communications (WPMC-2017

A cluster based communication architecture for distributed applications in mobile ad hoc networks

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006Includes bibliographical references (leaves: 63-69)Text in English; Abstract: Turkish and Englishx, 85 leavesIn this thesis, we aim to design and implement three protocols on a hierarchical architecture to solve the balanced clustering, backbone formation and distributed mutual exclusion problems for mobile ad hoc network(MANET)s. Our ¯rst goal is to cluster the MANET into balanced partitions. Clustering is a widely used approach to ease implemen-tation of various problems such as routing and resource management in MANETs. We propose the Merging Clustering Algorithm(MCA) for clustering in MANETs that merges clusters to form higher level of clusters by increasing their levels. Secondly, we aim to con-struct a directed ring topology across clusterheads which were selected by MCA. Lastly, we implement the distributed mutual exclusion algorithm based on Ricart-Agrawala algo-rithm for MANETs(Mobile RA). Each cluster is represented by a coordinator node on the ring which implements distributed mutual exclusion algorithm on behalf of any member in the cluster it represents. We show the operations of the algorithms, analyze their time and message complexities and provide results in the simulation environment of ns2

A Penalty-Based Approach to Handling Cluster Sizing in Mobile Ad Hoc Networks

In Mobile Ad Hoc Networks (MANETs) nodes are allowed to move freely which causes instability in the network. To handle this, the nodes are grouped into clusters which make the topology of the network appear more stable. In proposed algorithms, the size of these clusters has been either ignored or handled insufficiently. This Thesis proposes a penalty-based approach to handle cluster sizing in a more appropriate manner. A configurable penalty function is defined which assigns penalties to each of the possible cluster sizes. The penalty is then used in conjunction with a merge qualifier to determine if a merge is allowed. Merges will be allowed if the total penalty of the two clusters decreases as a result of the merge. Additionally a split merge process has been developed to allow a number of nodes to split from a cluster and merge with a new cluster. A separate split merge qualifier is used to determine if a split merge will be allowed to happen; it will as long as the total penalty of the two clusters after the split merge is less than the total penalty before the split merge. Simulations and thorough analysis of the results show that the proposed changes are on par with the base algorithm used; however, the penalty function allows for a more complex clustering sizing strategy

Fast community structure local uncovering by independent vertex-centred process

This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Minin