Self-stabilizing k-clustering in mobile ad hoc networks

In this thesis, two silent self-stabilizing asynchronous distributed algorithms are given for constructing a k-clustering of a connected network of processes. These are the first self-stabilizing solutions to this problem. One algorithm, FLOOD, takes O( k) time and uses O(k log n) space per process, while the second algorithm, BFS-MIS-CLSTR, takes O(n) time and uses O(log n) space; where n is the size of the network. Processes have unique IDs, and there is no designated leader. BFS-MIS-CLSTR solves three problems; it elects a leader and constructs a BFS tree for the network, constructs a minimal independent set, and finally a k-clustering. Finding a minimal k-clustering is known to be NP -hard. If the network is a unit disk graph in a plane, BFS-MIS-CLSTR is within a factor of O(7.2552k) of choosing the minimal number of clusters; A lower bound is given, showing that any comparison-based algorithm for the k-clustering problem that takes o( diam) rounds has very bad worst case performance; Keywords: BFS tree construction, K-clustering, leader election, MIS construction, self-stabilization, unit disk graph

A Survey of Checkpointing Algorithms in Mobile Ad Hoc Network

Checkpoint is defined as a fault tolerant technique that is a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at a later time. If there is a failure, computation may be restarted from the current checkpoint instead of repeating the computation from beginning. Checkpoint based rollback recovery is one of the widely used technique used in various areas like scientific computing, database, telecommunication and critical applications in distributed and mobile ad hoc network. The mobile ad hoc network architecture is one consisting of a set of self configure mobile hosts capable of communicating with each other without the assistance of base stations. The main problems of this environment are insufficient power and limited storage capacity, so the checkpointing is major challenge in mobile ad hoc network. This paper presents the review of the algorithms, which have been reported for checkpointing approaches in mobile ad hoc network

Enhanced AODV Routing Protocol Using Leader Election Algorithm

Failure of communication link in mobile ADHOC network is major issue. For the failure of link the performance of network is degraded. Due to mobility of mobile node brake the communication link and path of routing is failed. For the repairing of routing node used various algorithm such as leader election, distributed and selection algorithm. The failure of link decease the performance of routing protocol in mobile ad-hoc network, for the improvement of quality of service in mobile ad-hoc network various authors proposed a different model and method for prediction of link. The prediction of link decreases the failure rate of mobile node during communication. The leader election algorithm plays a major role in link failure prediction algorithm the process of link failure prediction implied in form of distributed node distribution. Proposed a new link stability prediction method based on current link-related or user-related information in shadowed environments. The modified protocol acquired the process of thresholds priority Oder on the basic of neighbor’s node. The selection of neighbor node deepens on the mode operation in three sections. According to order of state create cluster of priority of group. After creation of group calculate average threshold value and compare each group value with minimum threshold value and pass the control message for communication. Through this process mode of activation state of node is minimized the time of route establishment and maintenance. The selection of proper node in minimum time and other node in sleep mode the consumption of power is reduces. We modified SBRP protocol for selection of node during on demand request node according to sleep and activation mode of communication. Each node locally assigned priority value of node. For the evaluation of performance used network simulator NS-2.35. And simulate two protocol one is AODV-LE protocol, these protocol patch are available for the simulation purpose. And another protocol is AODV-LE-ME. AODV-LE-ME protocol is modified protocol of leader election protocol for the selection of mobile node during the communication. DOI: 10.17762/ijritcc2321-8169.15016

A framework for proving the self-organization of dynamic systems

This paper aims at providing a rigorous definition of self- organization, one of the most desired properties for dynamic systems (e.g., peer-to-peer systems, sensor networks, cooperative robotics, or ad-hoc networks). We characterize different classes of self-organization through liveness and safety properties that both capture information re- garding the system entropy. We illustrate these classes through study cases. The first ones are two representative P2P overlays (CAN and Pas- try) and the others are specific implementations of \Omega (the leader oracle) and one-shot query abstractions for dynamic settings. Our study aims at understanding the limits and respective power of existing self-organized protocols and lays the basis of designing robust algorithm for dynamic systems

Vers une structuration auto-stabilisante des réseaux Ad Hoc

International audienceIn this paper, we present a self-stabilizing asynchronous distributed clustering algorithm that builds non-overlapping k-hops clusters. Our approach does not require any initialization. It is based only on information from neighboring nodes with periodic messages exchange. Starting from an arbitrary configuration, the network converges to a stable state after a finite number of steps. Firstly, we prove that the stabilization is reached after at most n+2 transitions and requires (u+1)* log(2n+k+3) bits per node, whereΔu represents node's degree, n is the number of network nodes and k represents the maximum hops number. Secondly, using OMNet++ simulator, we performed an evaluation of our proposed algorithm.Dans cet article, nous proposons un algorithme de structuration auto-stabilisant, distribuéet asynchrone qui construit des clusters de diamètre au plus 2k. Notre approche ne nécessite aucuneinitialisation. Elle se fonde uniquement sur l’information provenant des noeuds voisins à l’aided’échanges de messages. Partant d’une configuration quelconque, le réseau converge vers un étatstable après un nombre fini d’étapes. Nous montrons par preuve formelle que pour un réseau de nnoeuds, la stabilisation est atteinte en au plus n + 2 transitions. De plus, l’algorithme nécessite uneoccupation mémoire de (u + 1) log(2n + k + 3) bits pour chaque noeud u où u représente ledegré (nombre de voisins) de u et k la distance maximale dans les clusters. Afin de consolider lesrésultats théoriques obtenus, nous avons effectué une campagne de simulation sous OMNeT++ pourévaluer la performance de notre solution

Self-stabilization in self-organized multihop wireless networks

International audienceIn large scale multihop wireless networks, flat architectures are not scalable. In order to overcome this major drawback, clusterization is introduced to support self-organization and to enable hierarchical routing. When dealing with multihop wireless networks the robustness is a main issue due to the dynamicity of such networks. Several algorithms have been designed for the clusterization process. As far as we know, very few studies check the robustness feature of their clusterization protocols. Moreover, when it is the case, the evaluation is driven by simulations and never by a theoretical approach. In this paper, we show that a clusterization algorithm, that seems to present good properties of robustness, is self-stabilizing. We propose several enhancements to reduce the stabilization time and to improve stability. The use of a Directed Acyclic Graph ensures that the self-stabilizing properties always hold regardless of the underlying topology. These extra criterion are tested by simulations

Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

A Self-Stabilizing K-Clustering Algorithm Using an Arbitrary Metric (Revised Version of RR2008-31)

32 pagesMobile ad hoc networks as well as grid platforms are distributed, changing, and error prone environments. Communication costs within such infrastructure can be improved, or at least bounded, by using k-clustering. A k-clustering of a graph, is a partition of the nodes into disjoint sets, called clusters, in which every node is distance at most k from a designated node in its cluster, called the clusterhead. A self-stabilizing asynchronous distributed algorithm is given for constructing a k-clustering of a connected network of processes with unique IDs and weighted edges. The algorithm is comparison-based, takes O(nk) time, and uses O(log n + log k) space per process, where n is the size of the network. This is the first distributed solution to the k-clustering problem on weighted graphs

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms