Greedy Solution Research Articles

Cross-Layer Data Delivery in Satellite-Aerial-Terrestrial Communication

The emerging SAT communication network holds great promise in various wireless access applications and services. How to achieve in such integrated network the best data delivery performance across multiple layers, given the limited inter-layer link capacity, however, is still a challenging issue. To the best of our knowledge, the cross-layer gateway selection problem for traffic transmission remains almost untouched, since previous works in this line mainly focused on one single layer and neglected the other layers. Toward this end, we first define in this article the cross-layer gateway selection as a constrained optimization problem in a SAT network, and then propose a greedy solution to efficiently identify an optimal set of gateways from an aerial network that serves as a relay layer for the data delivery between the terrestrial layer and the satellite layer. After providing extensive numerical results to validate the efficacy of our solution, we also point out challenges and future directions.

Submodularity of Distributed Join Computation.

We study distributed equi-join computation in the presence of join-attribute skew, which causes load imbalance. Skew can be addressed by more fine-grained partitioning, at the cost of input duplication. For random load assignment, e.g., using a hash function, fine-grained partitioning creates a tradeoff between load expectation and variance. We show that minimizing load variance subject to a constraint on expectation is a monotone submodular maximization problem with Knapsack constraints, hence admitting provably near-optimal greedy solutions. In contrast to previous work on formal optimality guarantees, we can prove this result also for self-joins and more general load functions defined as weighted sum of input and output. We further demonstrate through experiments that this theoretical result leads to an effective algorithm for the problem of minimizing running time, even when load is assigned deterministically.

A 3D approximate hybrid algorithm for stope boundary optimization

Determining stope boundaries is one of the critical steps to be taken when an underground mining method is selected; because of their significant impact on the profitability of the mining project, the stope boundaries have to be optimum to achieve maximum profits. This paper introduces a new hybrid algorithm that is a combination of dynamic programming and greedy algorithm. Although this proposed algorithm may fail to provide a true optimum solution, it generates better solutions than existing algorithms do. The new proposed algorithm and three existing algorithms are used to find the optimal stope boundaries on a real case ore body. The results demonstrate that the proposed algorithm can improve the profit by 117.78%, 16.86% and 0.42% compared to Floating Stope, Maximum Value Neighborhood (MVN), and Greedy algorithm solutions, respectively, on a real case study at a reasonable CPU time.

Reputation-Aware, Trajectory-Based Recruitment of Smart Vehicles for Public Sensing

With the abundant on-board resources in smart vehicles, they have become major candidates for providing ubiquitous services, including public sensing. One of the challenges facing such ubiquitous utilization is the recruitment of the participating vehicles. In this paper, we present the reputation-aware, trajectory-based recruitment (RTR) framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget cap. The framework consists of a reputation assessment scheme, a pricing model, and a selection scheme collaborating for a main recruitment objective; maximizing coverage with minimum cost. We propose greedy heuristic solutions targeting the selection problem in real-time. The RTR framework generalizes the basic selection problem to handle some practical scenarios, including departing vehicles and varying redundancy requirements. We also propose a reputation assessment scheme and a pricing model as parts of the framework. Extensive performance evaluation of the proposed framework is conducted and the evaluation shows that the proposed greedy heuristics are able to achieve results close to previously obtained optimal benchmarks under different scenarios, and that the framework succeeds in achieving high levels of coverage even when vehicles do not stick to their announced trajectories.

USA: Faster update for SDN-based internet of things sensory environments

Internet of Things (IoT) require ubiquitous and seamless network connectivity. Meanwhile, it also asks for effective service management, data transmission and analysis. Towards this end, software defined networks (SDN) technology is introduced as a key solution to enable IoT network management. When IoT requires an efficient forwarding policy reconfiguration as the response to the alteration of system requirement (e.g., network environment or user demand), SDN is able to adjust the configurations easily to meet its demand. Previous research efforts tried to complete the reconfiguration as quickly as possible, since the update speed is critical to the performance of network management. However, update time can be further reduced. In this paper, we propose a novel update mechanism, which is referred to as rule co-existence update. It is able to enlarge the solution space and obtain more individual solutions, without any negative effect to the packet headers or throughput of flows. Then, we propose a scheme called USA (i.e., Update Speed Accelerator), as a novel algorithm for network updates based on the above-mentioned rule co-existence, that accelerates the update of forwarding policy with spare rule space on current network switches for general IoT environments. Based on the obtained suboptimal greedy solution, USA shows a novel way for rule co-existence to accelerate the update of forwarding policies. It also proposes a simplified algorithm to ensure the forwarding correctness during the update process. Extensive simulation results show that USA can clearly reduce the update time for nearly half of policies in our experiments with few rule space overhead, which is less than 8% of all policies, and maximum update time is less than previous works when spare rule space is limited.

On the Shapley Value of Unrooted Phylogenetic Trees.

The Shapley value, a solution concept from cooperative game theory, has recently been considered for both unrooted and rooted phylogenetic trees. Here, we focus on the Shapley value of unrooted trees and first revisit the so-called split counts of a phylogenetic tree and the Shapley transformation matrix that allows for the calculation of the Shapley value from the edge lengths of a tree. We show that non-isomorphic trees may have permutation-equivalent Shapley transformation matrices and permutation-equivalent null spaces. This implies that estimating the split counts associated with a tree or the Shapley values of its leaves does not suffice to reconstruct the correct tree topology. We then turn to the use of the Shapley value as a prioritization criterion in biodiversity conservation and compare it to a greedy solution concept. Here, we show that for certain phylogenetic trees, the Shapley value may fail as a prioritization criterion, meaning that the diversity spanned by the top k species (ranked by their Shapley values) cannot approximate the total diversity of all n species.

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

BackgroundCombinatorial Interaction Testing (CIT) approaches have drawn attention of the software testing community to generate sets of smaller, efficient, and effective test cases where they have been successful in detecting faults due to the interaction of several input parameters. Recent empirical studies show that greedy algorithms are still competitive for CIT. It is thus interesting to investigate new approaches to address CIT test case generation via greedy solutions and to perform rigorous evaluations within the greedy context.MethodsWe present a new greedy algorithm for unconstrained CIT, T-TupleReallocation (TTR), to generate CIT test suites specifically via the Mixed-value Covering Array (MCA) technique. The main reasoning behind TTR is to generate an MCA M by creating and reallocating t-tuples into this matrix M, considering a variable called goal (ζ). We performed two controlled experiments addressing cost-efficiency and only cost. Considering both experiments, we did 3200 executions related to 8 solutions. In the first controlled experiment, we compared versions 1.1 and 1.2 of TTR in order to check whether there is significant difference between both versions of our algorithm. In such experiment, we jointly considered cost (size of test suites) and efficiency (time to generate the test suites) in a multi-objective perspective. In the second controlled experiment we confronted TTR 1.2 with five other greedy algorithms/tools for unconstrained CIT: IPOG-F, jenny, IPO-TConfig, PICT, and ACTS. We performed two different evaluations within this second experiment where in the first one we addressed cost-efficiency (multi-objective) and in the second only cost (single objective).ResultsResults of the first controlled experiment indicate that TTR 1.2 is more adequate than TTR 1.1 especially for higher strengths (5, 6). In the second controlled experiment, TTR 1.2 also presents better performance for higher strengths (5, 6) where only in one case it is not superior (in the comparison with IPOG-F). We can explain this better performance of TTR 1.2 due to the fact that it no longer generates, at the beginning, the matrix of t-tuples but rather the algorithm works on a t-tuple by t-tuple creation and reallocation into M.ConclusionConsidering the metrics we defined in this work and based on both controlled experiments, TTR 1.2 is a better option if we need to consider higher strengths (5, 6). For lower strengths, other solutions, like IPOG-F, may be better alternatives.

Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem

The p-center problem consists of choosing a subset of vertices in an undirected graph as facilities in order to minimize the maximum distance between a client and its closest facility. This paper presents a greedy randomized adaptive search procedure with path-relinking (GRASP/PR) algorithm for the p-center problem, which combines both GRASP and path-relinking. Each iteration of GRASP/PR consists of the construction of a randomized greedy solution, followed by a tabu search procedure. The resulting solution is combined with one of the elite solutions by path-relinking, which consists in exploring trajectories that connect high-quality solutions. Experiments show that GRASP/PR is competitive with the state-of-the-art algorithms in the literature in terms of both solution quality and computational efficiency. Specifically, it virtually improves the previous best known results for 10 out of 40 large instances while matching the best known results for others.

Regulating power management in interconnected microgrids

An effective regulating power management is needed in next generation interconnected microgrids to mitigate the effect of load-generation imbalance in the system. The objective of this paper is to present a two-stage regulating power management scheme in an interconnected microgrid (MG) system. First, in a day-ahead power management, the power generation set-points are determined in the individual MGs considering predicted demand and power from renewable energy sources. Second, in a real-time regulating power management, a network operator establishes a set of power flow interactions among MGs to mitigate the load-generation imbalances in MGs. These interactions are obtained using a convex multi-constraint optimization problem and a cooperative developed algorithm. A simple suboptimal greedy solution is also proposed. Numerical results demonstrate the effectiveness of the proposed real-time regulating power management in cooperation with day-ahead scheduling to reduce the system load-generation imbalance, in comparison with conventional power resources.

Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study

This study proposes a stochastic modeling approach as an evacuation decision support system to determine the required vehicles, scheduling and routes under uncertainties in evacuee population, time windows and bushfire propagation. The proposed model also considers road availability and disruptions. A greedy solution method is developed to cope with the complex nature of vehicle routing problem. Furthermore, the effectiveness of the proposed solution is evaluated by comparison with a designed genetic algorithm on sets of various numerical examples. The model is then applied on the real case study of the 2009 Black Saturday bushfires in Victoria, Australia. Several plausible evacuation scenarios are generated, utilizing the historical data of Black Saturday. The results are analyzed using the frequency approach to determine the optimal evacuation plan. The results show that it would have been possible to evacuate the late evacuees on Black Saturday, even within hard time windows and a maximum population.

Variants of k-regular nearest neighbor graph and their construction

Neighborhood graph and its construction play an important role in numerous problems. Many methods have been proposed to improve the classic k-NN construction based on various criteria. Recently, a new structure named k-regular nearest neighbor (k-RNN) graph has been proposed not only to minimize the sum of edge weights (for keeping the creditable neighborhood relationships), but also to require the node degree to be approximately k. However, the proposed graph construction algorithm is highly heuristic, which neither minimizes the cost (sum of edge weights) in an explicit manner nor formalizes the node degree requirement. In this work we formalize the essential requirements behind k-RNN. Several variants of this formalization will be studied, all being formulated as problems of energy function minimization with efficient greedy solutions. An experimental evaluation is also presented to demonstrate the effectiveness of our methods and superior performance compared to previous works.

Solving real-world sized container pre-marshalling problems with an iterative deepening branch-and-bound algorithm

Container terminals around the world regularly re-sort the containers they store according to their retrieval times in a process called pre-marshalling, thus ensuring containers are efficiently transferred through the terminal. State-of-the-art algorithms struggle to find optimal solutions for real-world sized pre-marshalling problems. To this end, we introduce an improved exact algorithm using an iterative deepening branch and bound search, including a novel lower bound computation, a new branching heuristic, new dominance rule and a new greedy partial solution completion heuristic. Our approach finds optimal solutions for 161 more instances than the state-of-the-art algorithm on two well known, difficult pre-marshalling datasets, and solves all instances in three other datasets in just several seconds. Furthermore, we find optimal solutions for a majority of real-world sized instances, and feasible solutions with very low relaxation gaps on those instances where no optimal could be found.

α-Overlapping area coverage for clustered directional sensor networks

Area coverage problem in Directional Sensor Networks (DSNs) presents great research challenges including minimization of number of active sensors and overlapping sensing coverage area among them, determination of their active sensing directions in an energy-efficient way, etc. Existing solutions permit to execute coverage enhancement algorithms at each individual sensor nodes, leading to high communication and computation overheads, loss of energy and reduced sensing coverage. In this paper, we first formulate the problem of maximizing area coverage with minimum number of active nodes as a mixed-integer linear programming (MILP) optimization problem for a clustered DSN. Due to its NP-completeness, we then develop a greedy alternate solution, namely α-overlapping area coverage (α-OAC). In α-OAC, each cluster head (CH) takes the responsibility of determining the active member nodes and their sensing directions, where, each sensing node is allowed to have at most α% coverage overlapping with its neighbors. The α-OAC CHs activate a sensor node iif the later has sufficient residual energy and send other member nodes to the sleep state. The proposed α-OAC system is distributed and scalable since it requires single-hop neighborhood information only. Results from extensive simulations, done in NS-3, reveal that the α-OAC system outperforms state-of-the-art works in terms of area coverage, network lifetime and operation overhead.

Surveillance camera location problem for route-flow observation in urban transportation networks: bi-level formulation and solution algorithm

ABSTRACTThis paper deals with the surveillance camera location problem in an urban transportation network. In a bi-level model, it is intended to maximise route-flow observation in the upper-level problem, while simultaneously taking into account the drivers’ response to the position of cameras in the lower level. To solve the problem, a greedy solution algorithm is proposed that iteratively assigns the new surveillance camera to the link with maximum ‘net’ traffic flow, and updates the equilibrium traffic flows according to the drivers’ response. The proposed algorithm can be applied, in a heuristic approach, to the networks in which there are unique path-flow solutions for the majority of O–D pairs. Numerical experiments are conducted over small examples as well as the Chicago Sketch network. The results support that, due to application of equilibrium paths information, the solution algorithm reveals a very fast performance in large scale. For 50 and 500 cameras in the Chicago Sketch network, the proposed algorithm finds solutions with 26.79% and 84.32% of observed demand, respectively.

Influence Maximization in Trajectory Databases

In this paper, we study a novel problem of influence maximization in trajectory databases that is very useful in precise location-aware advertising. It finds $k$ best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and approximate solutions to find the best set of trajectories. In the exact solution, we devise an expansion-based framework that enumerates trajectory combinations in a best-first manner and propose three types of upper bound estimation techniques to facilitate early termination. In addition, we propose a novel trajectory index to reduce the influence calculation cost. To support large $k$ , we propose a greedy solution with an approximation ratio of (1 − 1/e), whose performance is further optimized by a new proposed cluster-based method. We also propose a threshold method that can support any approximation ratio $\epsilon \in (0,1]$ . In addition, we extend our problem to support the scenario when there are a group of advertisements. In our experiments, we use real datasets to construct user profiles, motion patterns, and trajectory databases. The experimental results verified the efficiency of our proposed methods.

On the Complexity of Optimal Request Routing and Content Caching in Heterogeneous Cache Networks

In-network content caching has been deployed in both the Internet and cellular networks to reduce content-access delay. We investigate the problem of developing optimal joint routing and caching policies in a network supporting in-network caching with the goal of minimizing expected content-access delay. Here, needed content can either be accessed directly from a back-end server (where content resides permanently) or be obtained from one of multiple in-network caches. To access content, users must thus decide whether to route their requests to a cache or to the back-end server. In addition, caches must decide which content to cache. We investigate two variants of the problem, where the paths to the back-end server can be considered as either congestion-sensitive or congestion-insensitive, reflecting whether or not the delay experienced by a request sent to the back-end server depends on the request load, respectively. We show that the problem of optimal joint caching and routing is NP-complete in both cases. We prove that under the congestion-insensitive delay model, the problem can be solved optimally in polynomial time if each piece of content is requested by only one user, or when there are at most two caches in the network. We also identify the structural property of the user-cache graph that makes the problem NP-complete. For the congestion-sensitive delay model, we prove that the problem remains NP-complete even if there is only one cache in the network and each content is requested by only one user. We show that approximate solutions can be found for both cases within a $(1-1/e)$ factor from the optimal, and demonstrate a greedy solution that is numerically shown to be within 1% of optimal for small problem sizes. Through trace-driven simulations, we evaluate the performance of our greedy solutions to joint caching and routing, which show up to 50% reduction in average delay over the solution of optimized routing to least recently used caches.

Perfectly Periodic Scheduling of Collective Data Streams

This paper addresses the problem of scheduling a single resource to handle requests for time-sensitive periodic services (i.e., data streams) jointly realizing a distributed application. We specifically consider the case, where the demand of each data stream is expressed as a weight relative to a network-wide cyclic schedule. Within this context, we consider the problem of minimizing the schedule length while satisfying the perfect periodicity constraints: the service intervals for the same data stream are fixed and each data stream is cyclically served exactly as many times as its demand. This problem is challenging, as serving a data stream in one time slot might enforce serving it at some specific time slots in the future. As a result, most of the existing solutions have relaxed either the periodicity or the demand constraints of the data streams. In contrast, we study the strict enforcement of both requirements through perfectly periodic schedules. We show that the considered problem is NP-hard and address special cases for which optimal schedules can be derived. We further discuss the more generic instance of the problem represented by an arbitrary number of data streams and demands. Specifically, we provide an approximation algorithm and an efficient greedy solution for such a general case of arbitrary weights. We conduct extensive simulations to evaluate the performance of the proposed solutions. Finally, we show that it is possible to relax the input demands to improve the communication performance at the cost of some other overhead (e.g., in terms of energy consumption).

Energy-Aware Gateway Placement in Green Wireless Mesh Networks

In this letter, we address the following problem: given a mesh network deployment and $|\mathbb{G}|$ gateways to be added, what is the optimal gateway placement with the constraint of energy-minimization for green wireless mesh networks. Unlike previous research, which focuses on throughput optimization, we contribute by developing a mixed-integer linear programming (MILP) formulation, which satisfies the given flow demands while minimizing the global energy consumption of the network. The proposed solution is NP-hard; therefore, we also propose a heuristic-based greedy algorithm to efficiently solve large instances of this problem. To capture interference in the mesh network, we use the physical-interference model but employ a greedy algorithm to reduce the computation time for finding maximal independent sets. We implement both the MILP formulation and the greedy solution along with three other contemporary solutions in the area. Numerical results show that the proposed exact scheme provides the optimal result, while the greedy solution provides a solution within 5% of the optimal solution with just 1% computation time for green wireless mesh networks.

Fast-Lipschitz Power Control and User-Frequency Assignment in Full-Duplex Cellular Networks

In cellular networks, the three-node full-duplex transmission mode has the potential to increase spectral efficiency without requiring full-duplex capability of users. Consequently, three-node full-duplex in cellular networks must deal with self-interference and user-to-user interference, which can be managed by power control and user-frequency assignment techniques. This paper investigates the problem of maximizing the sum spectral efficiency by jointly determining the transmit powers in a distributed fashion, and assigning users to frequency channels. The problem is formulated as a mixed-integer nonlinear problem, which is shown to be non-deterministic polynomial-time hard. We investigate a close-to-optimal solution approach by dividing the joint problem into a power control problem and an assignment problem. The power control problem is solved by Fast-Lipschitz optimization, while a greedy solution with guaranteed performance is developed for the assignment problem. Numerical results indicate that compared with the half-duplex mode, both spectral and energy efficiencies of the system are increased by the proposed algorithm. Moreover, results show that the power control and assignment solutions have important, but opposite roles in scenarios with low or high self-interference cancellation. When the self-interference cancellation is high, user-frequency assignment is more important than power control, while power control is essential at low self-interference cancellation.

Tradeoff Between Sensing Quality and Network Lifetime for Heterogeneous Target Coverage Using Directional Sensor Nodes

Conventional researches on target coverage in directional sensor networks (DSNs) mainly focus to increase the network lifetime, overlooking the coverage quality of targets, especially they don’t consider the targets that have heterogeneous coverage requirements. Increasing sensing quality is of the utmost importance to ensure comfort living in smart cities. In this paper, we have designed a generalized framework, namely maximizing coverage quality with minimum number of sensors in DSN (MQMS-DSN) that has the ability to maximize the target coverage quality, or the network lifetime, or to make an efficient tradeoff in between the two following application demand. Using a probabilistic model for measuring the sensing coverage quality, we have developed optimal, suboptimal, and greedy solutions for MQMS problem. Empirical evaluations of the proposed MQMS systems have been carried out in network simulator version 3 (ns-3). The results show the effectiveness of the proposed systems compared with the state-of-the-art-works in terms of sensing quality and network lifetime.