Distributed Greedy Algorithm Research Articles

A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring

This paper investigates the multi-agent persistent monitoring problem via a novel distributed submodular receding horizon control approach. In order to approximate global monitoring performance, with the definition of sub-modularity, the original persistent monitoring objective is divided into several local objectives in a receding horizon framework, and the optimal trajectories of each agent are obtained by taking into account the neighborhood information. Specifically, the optimization horizon of each local objective is derived from the local target states and the information received from their neighboring agents. Based on the sub-modularity of each local objective, the distributed greedy algorithm is proposed. As a result, each agent coordinates with neighboring agents asynchronously and optimizes its trajectory independently, which reduces the computational complexity while achieving the global performance as much as possible. The conditions are established to ensure the estimation error converges to a bounded global performance. Finally, simulation results show the effectiveness of the proposed method.

Scaling the Construction of Wavelet Synopses for Maximum Error Metrics

Modern analytics involve computations over enormous numbers of data records. The volume of data and the stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the construction of synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. By approximating sharp discontinuities, wavelet decomposition has proved to be a very effective tool for data reduction. However, existing wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical complexities for large datasets. Furthermore, they cannot efficiently handle the multi-dimensional version of the problem. In order to provide a practical solution, we develop parallel algorithms that take advantage of key-properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present (i) a general framework for the parallelization of existing dynamic programming algorithms, (ii) a parallel version of one such DP algorithm, and (iii) two highly efficient distributed greedy algorithms that can deal with data of arbitrary dimensionality. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts.

Green content sharing mode: D2D Coordination Multiple Points Transmission

In next-generation 5G cellular networks, device-to-device (D2D) communication has emerged as an effective solution to support the growing popularity of multimedia contents for local service. Conventionally, the D2D content sharing mode is “one-to-one” matching, i.e, one demander will select one provider to request files from it. Under this mode, it is hard to cope with the growing demand for multimedia services for mobile users due to limited battery capacity for mobile devices. In this work, we propose an energy-efficient content sharing system via a novel D2D Coordination Multiple Points Transmission (D2D-CoMP), which shares content among multiple users to reduce the power consumption per user device. The highlights of this work lie in three parts. Firstly, the strategy for matching providers to demanders subject to self-interference constraints is formulated as a classical maximum weighted matching problem, in which the optimal solution can be derived when network-wide information is known, and also an effective distributed algorithm. Secondly, we design an optimal packet split algorithm for D2D-CoMP under comprehensive consideration of two aspects of communication efficiency and energy consumption to solve the problem how many data packets each provider transfers. Thirdly, we model the file reconstruction problem of collaboration demanders as the shortest Hamiltonian path problem and illustrate the file reconstruction process. Moreover, we develop a best-effort distributed greedy algorithm framework to find the shortest file reconstruction path. Importantly, numerical results demonstrate that the proposed mechanism can greatly reduce the energy consumption of each device with little or no increase in transmission delay.

Greedy Sparse Learning Over Network

In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted version of their individual intermediate estimates over the network. We provide a restricted-isometry-property (RIP)-based theoretical performance guarantee in the presence of additive noise. In the absence of noise, we show that under certain conditions on the RIP-constant of measurement matrix at each node of the network, the individual node estimates collectively converge to the true sparse signal. Furthermore, we provide an upper bound on the number of iterations required by the greedy algorithm to converge. Through simulations, we also show that the practical performance of the proposed algorithm is better than other state-of-the-art distributed greedy algorithms found in the literature.

Distributed Recovery of Jointly Sparse Signals Under Communication Constraints

The problem of the distributed recovery of jointly sparse signals has attracted much attention recently. Let us assume that the nodes of a network observe different sparse signals with common support; starting from linear, compressed measurements, and exploiting network communication, each node aims at reconstructing the support and the non-zero values of its observed signal. In the literature, distributed greedy algorithms have been proposed to tackle this problem, among which the most reliable ones require a large amount of transmitted data, which barely adapts to realistic network communication constraints. In this work, we address the problem through a reweighted $\ell_1$ soft thresholding technique, in which the threshold is iteratively tuned based on the current estimate of the support. The proposed method adapts to constrained networks, as it requires only local communication among neighbors, and the transmitted messages are indices from a finite set. We analytically prove the convergence of the proposed algorithm and we show that it outperforms the state-of-the-art greedy methods in terms of balance between recovery accuracy and communication load.

Deadline Aware Retransmission Threshold Setting Protocol in Cyber-Physical Systems

Many time-sensitive applications impose high requirement on real-time response. There exist many algorithms and routing protocols for efficient data packet delivery. However, previous works set the same retransmission threshold for all the relay nodes along a delivery path. The method decreases the probability of a packet being transmitted through the delivery path within given deadline. In this paper, we focus on finding the optimal retransmission thresholds for the relay nodes, such that the summation of the probability of a packet being transmitted to the next relay node or destination node within the specified deadline is maximized. A distributed greedy algorithm that can be run on sensor node is proposed, which enables the sensor node to adaptively set the optimal retransmission threshold. To avoid dropping the packet forwarded to the destination within given deadline with high probability, we develop a packet dropped protocol based on probabilistic delay bound. Experimental results show that the proposed protocols have better performance.

Hybrid matching pursuit for distributed through-wall radar imaging

In this paper, we consider the problem of through-wall radar imaging (TWRI) with an antenna array and develop a distributed greedy algorithm named hybrid matching pursuit (HMP). By dividing all the antenna phase centers into some groups, the task of TWRI can be formulated as a problem of jointly sparse signal recovery based on distributed data subsets. In TWRI applications, existing distributed greedy algorithms such as the simultaneous orthogonal matching pursuit (SOMP) algorithm and the simultaneous subspace pursuit (SSP) algorithm suffer from high artifacts and low-resolution, respectively. The proposed HMP algorithm aims to combine the strengths of SOMP and SSP (i.e., the orthogonality among selected basis-signals and the backtracking strategy for basis-signal reevaluation) and, accordingly, to reconstruct high-resolution radar images with no visible artifacts. Experimental results on real through-wall radar data show that, compared to existing greedy methods, the proposed HMP algorithm significantly improves the radar image quality, at the cost of increased computational complexity.

Distributed greedy pursuit algorithms

For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes. Based on this signal model along with a brief survey of existing greedy algorithms, we develop distributed greedy algorithms with low communication overhead. Incorporating appropriate modifications, we design two new distributed algorithms where the local algorithms are based on appropriately modified existing orthogonal matching pursuit and subspace pursuit. Further, by combining advantages of these two local algorithms, we design a new greedy algorithm that is well suited for a distributed scenario. By extensive simulations we demonstrate that the new algorithms in a sparsely connected network provide good performance, close to the performance of a centralized greedy solution.

Voronoi-based coverage improvement approach for wireless directional sensor networks

Differing from general omnidirectional wireless sensor networks, in the directional sensor networks, the effective sensing range of sensors is characterized by directionality and sensing angle. Therefore, there are dissimilar conditions for the discussion and research on the sensing coverage of directional sensor networks. This study used the characteristics of Voronoi diagram and direction-adjustable directional sensors to propose a distributed greedy algorithm, which can improve the effective field coverage of directional sensor networks. The sensor field is divided into Voronoi cells by the calculation of sensors, and the sensor working direction is evaluated based on Voronoi vertices. Considering the coverage contribution of convex polygonal cell of sensors and the coverage overlap of direction select between neighbor sensors, the working direction is adjusted and controlled, so as to improve the overall sensing field coverage ratio in the sensor network environment without global information. This study used simulation to change various parameters, such as the number of sensors, angle of view, and sensing radius, in the directional sensor network field to evaluate the efficiency of the proposed algorithm, and further analyzed and compared the improvement results of the overall sensing field coverage ratio.

On co-channel and adjacent channel interference mitigation in cognitive radio networks

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation–linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.

Local Greedy Approximation for Scheduling in Multihop Wireless Networks

In recent years, there has been a significant amount of work done in developing low-complexity scheduling schemes to achieve high performance in multihop wireless networks. A centralized suboptimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because its empirically observed performance is close to optimal in a variety of network settings. However, its distributed realization requires high complexity, which becomes a major obstacle for practical implementation. In this paper, we develop simple distributed greedy algorithms for scheduling in multihop wireless networks. We reduce the complexity by relaxing the global ordering requirement of GMS, up to near zero. Simulation results show that the new algorithms approximate the performance of GMS, and outperform the state-of-the-art distributed scheduling policies.

Efficient broadcasting in multi-hop wireless networks with a realistic physical layer

Almost all existing broadcasting algorithms assume an ideal physical layer, in which a successful transmission is guaranteed if the distance between communicating nodes is less than a certain threshold, e.g., a transmission range. However, wireless communication links normally suffer from the characteristics of realistic physical layer, which significantly reduce the reliability of broadcasting among the nodes. This work addresses the minimal broadcasting problem in multi-hop wireless networks with a realistic physical layer. Given a probability p*, the problem is to design a distributed broadcasting algorithm such that each node in the network receives the broadcasting packet with probability no less than p* and the number of retransmissions is minimized. We show that this problem is NP-hard and propose a distributed greedy algorithm which maximizes the gain cost ratio at each node. We prove that the proposed algorithm guarantees that each node receives the broadcasting packet with probability no less than p*, and analyze upper bound on the number of total retransmissions in the network. Simulation results show that our algorithm can provide near 100% coverage to the wireless network with a realistic physical layer, and reduce the number of retransmissions compared with modified traditional flooding schemes k-Flooding (pure flooding with multiple times) and ACK-Flooding (pure flooding with acknowledgement). We believe our algorithmic solution is efficient and practical for general existing multi-hop wireless networks.

Real-time data aggregation in contention-based wireless sensor networks

We investigate the problem of delay constrained maximal information collection for CSMA-based wireless sensor networks. We study how to allocate the maximal allowable transmission delay at each node, such that the amount of information collected at the sink is maximized and the total delay for the data aggregation is within the given bound. We formulate the problem by using dynamic programming and propose an optimal algorithm for the optimal assignment of transmission attempts. Based on the analysis of the optimal solution, we propose a distributed greedy algorithm. It is shown to have a similar performance as the optimal one.

Constructing special k-dominating sets using variations on the greedy algorithm

This paper focuses on the efficient selection of a special type of subset of network nodes, which we call a k -SPR set, for the purpose of coordinating the routing of messages through a network. Such a set is a special k -hop-connected k -dominating set that has an additional property that promotes the regular occurrence of routers in all directions. The distributed algorithms introduced here for obtaining a k -SPR set require that each node broadcast at most three messages to its k -hop neighbors. These transmissions can be made asynchronously. The time required to send these messages and the sizes of the resulting sets are compared by means of data collected from simulations. The main contribution is the adaptation of some variations of the distributed greedy algorithms to the problem of generating a small k -SPR set. These variations are much faster than the standard distributed greedy algorithm. Yet, when used with a sensible choice for a certain parameter, our empirical evidence strongly suggests that the resulting set size will generally be very close to the set size for the standard greedy algorithms.

Distributed media rate allocation in multipath networks

The paper addresses the distributed path computation and rate allocation problems for video delivery over multipath networks. The streaming rate on each path is determined such that the end-to-end media distortion is minimized, when a media client aggregates packets received via multiple network channels to the streaming server. In common practical scenarios, it is, however, difficult for the server to have the full knowledge about the network status. Therefore, we propose here a distributed path selection and rate allocation algorithm, where the network nodes participate to the optimized path selection and rate allocation based on their local view of the network. This eliminates the need for end-to-end network monitoring, and permits the deployment of large scale rate allocation solutions. We design a distributed algorithm for optimized rate allocation, where the media client iteratively determines the best set of streaming paths, based on information gathered by network nodes. Each intermediate node then forwards incoming media flows on the outgoing paths, in a distributed manner. The proposed algorithm is shown to quickly converge to the rate allocation that provides a maximal quality to the video client. We also propose a distributed greedy algorithm that achieves close-to-optimal end-to-end distortion performance in a single pass. Both algorithms are shown to outperform simple heuristic-based rate allocation approaches for numerous random network topologies. They offer an interesting solution for media-specific rate allocation over large scale multipath networks.

The Network as a Storage Device: Dynamic Routing with Bounded Buffers

We study dynamic routing in store-and-forward packet networks where each network link has bounded buffer capacity for receiving incoming packets and is capable of transmitting a fixed number of packets per unit of time. At any moment in time, packets are injected at various network nodes with each packet specifying its destination node. The goal is to maximize the throughput, defined as the number of packets delivered to their destinations. In this paper, we make some progress on throughput maximization in various network topologies. Let n and m denote the number of nodes and links in the network, respectively. For line networks, we show that Nearest-to-Go (NTG), a natural distributed greedy algorithm, is $\tilde{O}(\sqrt{n})$-competitive, essentially matching a known $\Omega(\sqrt{n})$lower bound on the performance of any greedy algorithm. We also show that if we allow the online routing algorithm to make centralized decisions, there is a randomized polylog(n)-competitive algorithm for line networks as well as for rooted tree networks, where each packet is destined for the root of the tree. For grid graphs, we show that NTG has a competitive ratio of $\tilde{\Theta}(n^{2/3})$while no greedy algorithm can achieve a ratio better than $\Omega(\sqrt{n})$. Finally, for arbitrary network topologies, we show that NTG is $\tilde{\Theta}(m)$-competitive, improving upon an earlier bound of O(mn).

Coverage by directional sensors in randomly deployed wireless sensor networks

We study a novel “coverage by directional sensors” problem with tunable orientations on a set of discrete targets. We propose a Maximum Coverage with Minimum Sensors (MCMS) problem in which coverage in terms of the number of targets to be covered is maximized whereas the number of sensors to be activated is minimized. We present its exact Integer Linear Programming (ILP) formulation and an approximate (but computationally efficient) centralized greedy algorithm (CGA) solution. These centralized solutions are used as baselines for comparison. Then we provide a distributed greedy algorithm (DGA) solution. By incorporating a measure of the sensors residual energy into DGA, we further develop a Sensing Neighborhood Cooperative Sleeping (SNCS) protocol which performs adaptive scheduling on a larger time scale. Finally, we evaluate the properties of the proposed solutions and protocols in terms of providing coverage and maximizing network lifetime through extensive simulations. Moreover, for the case of circular coverage, we compare against the best known existing coverage algorithm.