Local Minimum Spanning Tree Research Articles

Clustering with Local Density Peaks-Based Minimum Spanning Tree

Clustering analysis has been widely used in statistics, machine learning, pattern recognition, image processing, and so on. It is a great challenge for most existing clustering algorithms to discover clusters with arbitrary shapes. Clustering algorithms based on Minimum spanning tree (MST) are able to discover clusters with arbitrary shapes, but they are time consuming and susceptible to noise points. In this paper, we employ local density peaks (LDP) to represent the whole data set and define a shared neighbors-based distance between local density peaks to better measure the dissimilarity between objects on manifold data. On the basis of local density peaks and the new distance, we propose a novel MST-based clustering algorithm called LDP-MST. It first uses local density peaks to construct MST and then repeatedly cuts the longest edge until a given number of clusters are found. The experimental results on synthetic data sets and real data sets show that our algorithm is competent with state-of-the-art methods when discovering clusters with complex structures.

Distributed Data Gathering Algorithm Based on Spanning Tree

The energy consumption of the nodes is limited in wireless sensor networks (WSNs). Since the sink node takes up most of the data forwarding, the energy consumption is maximum and it is easily formed around the energy hole. The network lifetime is short; so how to improve the network effective node energy and prolong the network lifetime have become a hot issue in the application of WSNs. In this article, our aim is to construct an optimal spanning tree of maximum lifetime as a research object. We propose a kind of accurately distributed data gathering algorithm-based tree. Compared with the existing local minimum spanning tree (LMST) distributed algorithm and random routing algorithm, our proposed constructing method based on spanning tree can prolong the lifetime of WSNs.

High accuracy correspondence field estimation via MST based patch matching

This paper presents an effective framework for correspondence field estimation. The core idea is to construct pixel-level and superpixel-level patch matching to achieve high accuracy estimation as well as fast speed computation. To this end, a hybrid edge-preserving supported weighting approach is first developed, which contributes to better performance on the pixel level, especially on those in the regions of fine structures. Then, a local Minimum Spanning Tree (MST) is constructed to describe regions and develop the adaptive smooth penalty weights, so that the over-patching in large textureless regions can be effectively avoided. In addition, the MST is further extended to handle occlusions in way of edge preserving strategy. Finally, all the above treatments are collected into an optimization model where the objective function is developed in terms of Markov Random Filed (MRF). In computation, a fast yet efficient iterative optimization strategy is developed. Our approach achieves favorable place on optical flow benchmark, which locates at the top two and top four for endpoint error and angular error evaluations among more than 130 approaches listed in the webpage.

Research on the energy balance algorithm of WSN based on topology control

Topology control is a key energy-saving technology in the wireless-sensor networking. In this paper, networking-associated algorithm named local minimum spanning tree (LMST) employed while compromising on energy balance issues, leading to the short lifetime of the network, and proposes an energy balance algorithm. The algorithm fully investigates the energy utilization to link the data communication and the remaining energy of the communication node, to achieve the balanced energy, and to avoid the complexities associated with excessive consumption of energy when the intermediate node acts as a data forwarding task, thus extending the network life cycle. The simulated experimental outcomes showed a great potential of the proposed algorithm that can efficiently prolong the network lifetime and guarantee the long-term reliable operation of the network while ensuring other network performance indicators, such as throughput and delivery rate.

A New Energy Efficient Routing Scheme for Data Gathering

Sensor network consists of low cost battery powered nodes which is limited in power. Hence power efficient methods are needed for data gathering and aggregation in order to achieve prolonged network life. However, there are several energy efficient routing protocols in the literature; quiet of them are centralized approaches, that is low energy conservation. This paper presents a new energy efficient routing scheme for data gathering that combine the property of minimum spanning tree and shortest path tree-based on routing schemes. The efficient routing approach used here is Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) which is robust and localized. This is based on powerful localized structure, local minimum spanning tree (LMST). The actual routing tree is constructed over this topology. There is also a solution involved for route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network.

IJARCCE - Computer and Communication Engineering

A Wireless ad-hoc network is an autonomous, self-organize distributed, peer to peer network of fix, nomadic or mobile user that communicates over bandwidth constrained wireless links. Energy Consumption and Network Connectivity are two of the most important issue that yet to be resolve in MANET's. Broadcasting, in context of ad-hoc networks is a costly operation and thus topology control has been proposed to achieve efficient broadcasting with low interference and energy consumption. By using topology control each node optimizes its transmission power, by maintaining network connectivity by localize manner. In this paper, we propose Local Tree-Based Reliable Topology (LTRT), which is mathematically proven to guarantee k-edge connectivity while preserving the feature of Local Minimum Spanning Tree (LMST).while applying topology control technology into Manet, it is important to address the issue of performance degradation due to node mobility. In the network each node contain the topology information to maintain the connectivity with neighbors for that it needs to be frequently and appropriately update its information according to its moving speed. In our mechanism, each node determines exact value of topology control update interval according to topology information of its neighbors. By simulation results using network simulator (NS2) we achieve better dynamic topology update mechanism.

A Topology Control Algorithm for Sensor Networks Based on Robust Optimization

In recent years, wireless sensor networks have been widely used in data acquisition, surveillance, event monitoring, and so forth. Topology control is an important issue in designing sensor networks. Considering the uncertainty of distance between nodes, a distributed topology control algorithm named as LRMST, which is based on the local minimum spanning tree (LMST) algorithm, is proposed by applying the 0-1 robust discrete optimization theory. Firstly, when only the cost coefficients are subject to uncertainty, it is proved that the robust counterpart of the 0-1 discrete optimization problem on n variables can be solved by solving at most [Formula: see text] deterministic problems, where Γ denotes the number of cost coefficients which change in an uncertainty set. Then we present a robust model for the MST problem under distance uncertainty. According to the proved conclusion, an algorithm is proposed to obtain the robust solution of uncertain MST problem by solving only one deterministic MST problem, after which LRMST algorithm is designed when the distance between nodes is affected by uncertainty. Simulation results show that LRMST algorithm tends to select some edges whose estimated distance is slightly longer and obtains the robustness when the distance is uncertain at the expense of less optimal value compared with LMST algorithm.

Dynamic Beacon Mobility Scheduling for Sensor Localization

In mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals and becomes localized with minimum delay. We propose a novel DeteRministic dynamic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, the beacon trajectory is defined as the track of Depth-First Traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT dynamically, under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to Received Signal Strength (RSS)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free, and derive the upper bound of beacon total moving distance in this case. Then, we suggest to apply node elimination and Local Minimum Spanning Tree (LMST) to shorten beacon tour and reduce delay. Further, we extend DREAMS to multibeacon scenarios. Beacons with different coordinate systems compete for localizing sensors. Loser beacons agree on winner beacons' coordinate system, and become cooperative in subsequent localization. All sensors are finally localized in a commonly agreed coordinate systems. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path-based scheduling method.

A Locally-Adjustable Planar Structure for Adaptive Topology Control in Wireless Ad Hoc Networks

In wireless ad hoc networks, the constructed topology is preferred to be planar since a planar topology enables guaranteed delivery of packets without a routing table. Previous planar structures are statically constructed for the whole network. However, environmental or network dynamics such as channel status, interference, or residual energy will prevent such structures from providing the best service to the network. In this paper, we present a t-adjustable planar structure (TAP) which enables each node to adjust the topology independently via a parameter t and allows nodes to have different path loss exponent. TAP is based on three well-known planar structures: Gabriel Graph, Relative Neighborhood Graph, and Local Minimum Spanning Tree. We show properties of TAP by proof or simulation: (1) It preserves connectivity; (2) it is planar, sparse, and symmetric; (3) it preserves all minimum energy path when t = 1 for all nodes; and (4) the average transmission power, interference, and node degree decrease as t increases and the maximum node degree is bounded by 6 when t = 3 for all nodes.

LTRT: An efficient and reliable topology control algorithm for ad-hoc networks

Broadcasting, in the context of ad-hoc networks, is a costly operation, and thus topology control has been proposed to achieve efficient broadcasting with low interference and low energy consumption. By topology control, each node optimizes its transmission power by maintaining network connectivity in a localized manner. Local Minimum Spanning Tree (LMST) is the state-of-the-art topology control algorithm, which has been proven to provide satisfactory performance. However, LMST almost always results in a 1-connected network, without redundancy to tolerate external factors. In this paper, we propose Local Tree-based Reliable Topology (LTRT), which is mathematically proven to guarantee k-edge connectivity while preserving the features of LMST. LTRT can be easily constructed with a low computational complexity of O(k(m + n log n)), where k is the connectivity of the resulting topology, n is the number of neighboring nodes, and m is the number of edges. Simulation results have demonstrated the efficiency of LTRT and its superiority over other localized algorithms.

Localized Interference-aware and Energy-conserving Topology Control Algorithms

Energy conservation and interference reduction are the two ultimate goals of topology control in wireless multihop networks. However, the existing energy-conserving algorithms rarely consider interference reduction or at most consider it implicitly. It has been proved that the power-efficient topology does not guarantee low interference. Considering that in any topology, the nodes affected by the communications between any other nodes should be as few as possible, we propose in this article two algorithms, the interference-aware local minimum spanning tree (MST) based algorithm (IALMST) and the interference-bounded energy-conserving algorithm (IBEC). In IALMST, each node builds its local MST independently according to the costs of interference and energy consumption, while in IBEC, each node commonly selects the edge with the least energy consumption, and only when the interference exceeds a predefined bound, it is allowed to select a more expensive edge to reduce interference. Theoretical analysis and simulations illustrate that both algorithms can effectively conserve energy and reduce interference.

Design and analysis of an MST-based topology control algorithm

In this paper, we present a minimum spanning tree (MST)-based algorithm, called local minimum spanning tree (LMST), for topology control in wireless multihop networks. In this algorithm, each node builds its LMST independently and only keeps on-tree nodes that are one-hop away as its neighbors in the final topology. We analytically prove several important properties of LMST: 1) the topology derived under LMST preserves the network connectivity; 2) the node degree of any node in the resulting topology is bounded by 6; and 3) the topology can be transformed into one with bidirectional links (without impairing the network connectivity) after removal of all unidirectional links. Simulation results show that LMST can increase the network capacity as well as reduce the energy consumption.

Finding minimum transmission radii for preserving connectivity and constructing minimal spanning trees in ad hoc and sensor networks

Ad hoc networks are normally modeled by unit graphs, where two nodes are connected if and only if their distance is at most the transmission radius R, equal for all nodes. Larger than necessary values of R cause communication interference and consumption of increased energy, while smaller values may disable data communication tasks such as routing and broadcasting. It was recognized that the minimum value of R that preserves the network connectivity is equal to the longest edge in the minimum spanning tree. However, all existing solutions for finding R rely on algorithms that require global network knowledge or inefficient straightforward distributed adaptations of centralized algorithms. This article proposes to use the longest LMST (local minimum spanning tree, recently proposed message free approximation of MST) edge to approximate R using a wave propagation quasi-localized algorithm. The differences between exact and so approximated values of R are estimated for two and three-dimensional random unit graphs. Despite small number of additional edges in LMST with respect to MST (under 5%), they can extend R by about 33% its range on networks with up to 500 nodes. We then prove that MST is a subset of LMST and describe a quasi-localized scheme for constructing MST from LMST. It needs less than 7 messages per node on average (for networks up to 500 nodes). The algorithm eliminates LMST edges which are not in MST by a loop breakage procedure, which iteratively follows dangling edges from leaves to LMST loops, and breaks loops by eliminating their longest edges, until the procedure finishes at a single node (as a byproduct, this single node can also be considered as an elected leader of the network). This so elected leader also learns longest MST edge in the process, and may broadcast it to other nodes. We also describe an algorithm for updating MST when a single node is added to the network.

Localized LMST and RNG based minimum-energy broadcast protocols in ad hoc networks

In the minimum energy broadcasting problem, each node adjusts its transmission power to minimize the total energy consumption while still guaranteeing the full coverage of the network. We consider both topology control and broadcast oriented protocols, for which all existing solutions require global network information. In this paper, we describe new localized protocols where nodes require only local informations about their neighborhood (distances or geographic positions). In addition to this, our protocols are shown experimentally to be comparable to the best known globalized BIP solution. Our solutions are based on the use of neighbor elimination scheme applied on the relative neighborhood graph (RNG) and local minimum spanning tree (LMST) which preserve connectivity and are defined in localized manner. Two variants are proposed, one with timeout applied on nodes receiving message from non-RNG (non-LMST) neighbor and retransmitting immediately otherwise (unless list of RNG or LMST neighbors in need of the message is empty), and one with timeout applied on all the nodes. We proved that LMST is a subset of RNG, which explains why LMST always performs better among the two.