Minimum Deployment Cost Research Articles

Urban 5G MmWave Networks: Line-of-Sight Probabilities and Optimal Site Locations

In this work, we implemented line-of-sight (LoS) ray tracing functionality to investigate problems in millimetre-wave propagation modelling and network planning in 3D city model environments. First, we validated an existing LoS propagation probability model expressed as an exponential rule with the link distance. By fitting ray tracing simulation results under different scenarios to the model, the relationships between key parameters in the model and factors including the building density and the transmitter height were qualitatively analysed. Next, we developed a network planning framework for a multi-hop outdoor urban network by formulating a mixed-integer linear programming problem which minimises the overall deployment cost through optimal site selection. Taking the sets of potential site locations and potential links as inputs, we selected a subset of the sites that comprise a tree-structured network that satisfies all the user demands at a minimum deployment cost. We also analysed the time required for solving this optimisation problem in order to provide a prediction of the execution time for larger-sized problems.

Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks (WSNs) greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery. The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN. However, existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space. Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN, we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage. The Confident Information Coverage (CIC) model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage (CICMTP) problem to minimize the deployed sensor nodes. As the CICMTP is NP-hard, we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC (LGTA-CIC) and Overall Greedy Search Algorithm based on CIC (OGSA-CIC). The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate. Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP, TPNP and EENP algorithms.

Cost-Optimal Deployment of Millimeter-Wave Base Stations Under Outage Requirement

Today’s growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave networks can carry a large volume of traffic, highly intermittent connectivity and the challenges related to installing many small-cell base stations (BSs) in urban geometry have impeded its progression into practical networks. To cope with these challenges, we present, in this paper, an approach to the mmWave BS deployment (site planning) problem, based on the minimum-deployment-cost criterion that is subject to user equipment (UE) outage constraints. Unlike the prior works, the proposed model captures the randomness of link blockage and signal-to-interference-plus-noise-ratio (SINR) statistics in mmWave networks. We formulate the minimum-cost deployment problem as large-scale integer nonlinear programming (INP). To deal with the coupled and combinatorial of the problem, the large-scale INP has approached to devise a suboptimal but efficient algorithm by decomposing it into two subproblems: (i) cell coverage optimization and (ii) minimum subset selection. We provide the solutions to each subproblem as well as theoretical justifications of them. Simulation results that illustrate UE outage guarantees of the proposed BS deployment method are presented. The results reveal that the proposed method uniquely distributes the macro-diversity orders that are distinct from other benchmarks.

Joint Shareability and Interference for Multiple Edge Application Deployment in Mobile-Edge Computing Environment

Mobile-edge computing (MEC), as an emerging computing paradigm, allows app vendors to deploy their mobile and/or IoT applications on edge servers to deliver low-latency services to their app users. However, when an edge server needs to serve excessive app users concurrently, severe interference is incurred, which immediately reduces app users’ achievable data rates and, consequently, impacts their perceived service quality. This is a major challenge to the app vendor’s attempt to minimize the edge resources required for serving its app users with a satisfactory service quality. To tackle this challenge, in this article, we present and formulate this multiple edge application deployment (MEAD) problem in the MEC environment, aiming to maximize app users’ overall service quality at minimum deployment cost, considering application shareability and communication interference. We prove that the MEAD problem is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hard. Then, we propose a heuristic approach, namely, the deployment-priority greedy via the divide-and-conquer strategy (DPG-D&C), to solve the MEAD problem effectively and efficiently. We evaluate our approach extensively by using a widely used real-world data set. The experimental results show that DPG-D&C significantly outperforms state-of-the-art approaches.

Deployment Optimization Method of Multistatic Radar for Constructing Circular Barrier Coverage.

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied. Then, to optimize the deployment of the whole circular area, a model based on minimum deployment cost is proposed, and the proposed model is divided into two sub-models to solve the optimization issue. In the inner model, it is assumed that the width of a sub-circle is given. Based on the optimization conditions of the deployment pattern, integer linear programming (ILP) and exhaustive method (EM) are jointly adopted to determine the types and numbers of deployment patterns. Moreover, a modified formula is introduced to calculate the maximum valid number of receivers in a pattern, thus narrowing the search scope of the EM. In the outer model, the width of a sub-circle is assumed to be a variable, and the EM is adopted to determine the minimum total deployment cost and the optimal deployment patterns on each sub-circle. Moreover, the improved formula is exploited to determine the range of width for a sub-circle barrier and reduce the search scope of the EM. Finally, simulations are conducted in different conditions to verify the effectiveness of the proposed method. The simulation results indicate that the proposed method can spend less deployment cost and deploy fewer transmitters than the state-of-the-artwork.

Optimizing Quality-Aware Big Data Applications in the Cloud

The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions able to support data-intensive application. Competitiveness and innovation have strongly benefited from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently engage in data-intensive processes as part of their core businesses; however, fully embracing the data-driven paradigm is still cumbersome, and establishing a production-ready, fine-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. This paper proposes a novel tool, integrated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that efficiently and effectively explores the space of alternative Cloud configurations, seeking the minimum cost deployment that satisfies quality of service constraints. The soundness of the proposed solution has been thoroughly validated in a vast experimental campaign encompassing different applications and Big Data platforms.

Improved Coverage and Connectivity via Weighted Node Deployment in Solar Insecticidal Lamp Internet of Things

As an important physical control technology, solar insecticidal lamp (SIL) can effectively prevent and control the occurrence of pests. The combination of SILs and wireless sensor networks (WSNs) initiates a novel agricultural Internet of Things (IoT), i.e., SIL-IoTs, to simultaneously kill pests and transmit pest information. In this article, we study the weighted SIL deployment problem (wSILDP) in SIL-IoTs, where weighted locations on ridges are prespecified and some of them are selected to deploy SILs. Different from the existing studies whose optimization objective is to minimize the deployment cost, we consider the deployment cost and the total weight of selected locations jointly. We formulate the wSILDP as the weighted set cover (WSC) problem and propose a layered deployment method based on greedy algorithm (LDMGA) to solve the defined optimization problem. The LDMGA is composed of two phases. First, SILs are deployed layer by layer from the boundary to the center until the entire farmland is completely covered. Second, on the basis of three design operations, i.e., substitution, deletion and fusion, the suboptimal locations obtained in the first phase are fine-tuned to achieve the minimum deployment cost together with the maximum total weight for meeting the coverage and connectivity requirements. Simulation results clearly demonstrate that the proposed method outperforms three peer algorithms in terms of deployment cost and total weight.

Submarine seismic tomography using optical fibres

The repurpose of the available telecommunication fibre optical network as a seismic monitoring tool has been recently tested with very promising prospects. Optical fibre networks possess all the ingredients to become the next generation seismic monitoring system, offering high performance at minimum deployment and maintenance cost, especially at hardly accessible regions such as the oceans’ bottom.

Backup fibre deployment algorithm based on daily traffic demand in fibre‐wireless (FiWi) access networks

In fibre-wireless (FiWi) access networks, the achievement of survivability becomes critical because any failure of FiWi components may result in severe traffic interruptions. In this study, the authors proposed a new backup fibre deployment mechanism with the consideration of time-varying daily traffic demand. Firstly, a new remote node structure for the protection of switching is established to help the backup fibre deployment. Secondly, they formed a minimum deployment cost problem in terms of the daily traffic demand and interrupted traffic protection. Furthermore, a two-stage algorithm is proposed to solve this problem and obtain an optimal deployment pattern. Simulation results demonstrate that the proposed mechanism can reduce the cost of the backup fibre deployment and the delay of the interrupted traffic recovery significantly.

Cost-Optimal Deployment of a C-RAN With Hybrid Fiber/FSO Fronthaul

Centralized radio access network (C-RAN) has been considered as an architectural solution able to reduce capital and operational expenditure in dense 5G cellular networks while allowing better network performance. The C-RAN approach decouples baseband units from antenna sites and places them in selected locations, connected by the so-called fronthaul links. These links require expensive high-capacity connections, thus calling for cost-efficient deployment. This paper presents a hybrid fronthaul solution for C-RAN based on both optical fibers and free-space optics (FSO) to enhance fronthaul flexibility and minimize deployment costs. Two design strategies based on integer linear programming are proposed for both greenfield and brownfield deployments. The first strategy is referred to as joint planning (JP) and is based on the joint minimization of the number of deployed remote radio heads (RRHs) and the cost of the hybrid fiber/FSO fronthaul. The second strategy is based on two-step disjoint planning (DP) that first identifies a cost-optimal RRH placement and then finds the corresponding minimum cost deployment for the fronthaul links. Results obtained with JP and DP are compared in dense urban area scenarios (i.e., with characteristics similar to festivals or concerts), highlighting the advantage of the JP approach compared to DP, both in terms of costs and an enhanced flexibility during the network design process.

On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms

Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results.

Target Coverage-Oriented Deployment of Rechargeable Directional Sensor Networks With a Mobile Charger

With the advance on wireless energy transfer, it is reliable and favorable to power a directional sensor network (DSN) by wireless charging. This paper investigates how to deploy a rechargeable DSN using a mobile charger (MC) with the least number of nodes for perpetual target coverage subject to the limited sensing angles of directional sensors and limited energy capacity of the MC. We prove that the proposed problem is NP-hard. Next, we formulate it as a mixed integer nonlinear program to determine the smallest subset of sites to place sensors and the working directions of sensing nodes. Then, we propose two algorithms, i.e., an energy-bounded minimum-cost deployment and a relaxed-linear-program and repairing-based deployment. The simulation results demonstrate that the latter has higher success rate and solution quality than the former at the expense of more computational time.

All-optical generation and transmission of multiple ultrawideband signals over free space optical link

Ultrawideband (UWB) signals have low allowable transmission powers. To enhance the coverage and capacity of UWB signals, a cost-efficient solution for the access network has been proposed and simulated. The access network is based on gamma–gamma free-space optical channel model, which is employed between the central unit and the four different carrier-free radio access units (RAUs). All-optical generation and transmission of UWB signals at a rate of 1.5 Gbps to each RAU are demonstrated. Bit error rate results are obtained to observe the performance of the UWB signals received at the mobile stations. The proposed architecture requires minimum deployment cost and gives acceptable bit error rate results.

Computation of Mesh Node Placements Using DE Approach to Minimize Deployment Cost with Maximum Connectivity

A node placement problem is formulated to ensure maximum connectivity and minimum deployment cost using differential evolution based node placement with traffic weight algorithm. A simulation study is performed to evaluate the performance of the network under four different client distribution patterns (Normal, Uniform, Exponential and Weibull). A maximum throughput of 95.3% and 96.2% of throughput is achieved in normal and weibull distributions than the conventional placement. It is observed from the results that the two distributions have good impact on network performance with minimum deployment cost and maximum connectivity. The packet delivery rate shows a percentage increase of 36.6% compared to the SA based placement scheme in normal distribution. It is also observed that a percentage increase of 28.9% of improvement is achieved when clients are distributed with weibull distribution and minimum end to end delay.

Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage.

Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

A Proficient Node Deployment Mechanism Using Adjustable Sensing Range in Wireless Sensor Networks

In this paper, a proficient node deployment mechanism is proposed which covers all the target points using adjustable sensing ranges in grid-based wireless sensor networks. In the beginning, the sink and the target points are randomly deployed in a grid-based environment where locations are arbitrarily selected. At first, the ants are positioned at the sink location (grid point) from where the deployment of sensors starts. An ant can move in one of the four possible directions, i.e., {1 (left); 2 (right); 3 (top); 4 (bottom)} from the sink location depending on the maximum number of uncovered target points. In case there is no path to move forward, an existing sensor is selected as the next location corresponding to that next sensor point where a deployed sensor can cover maximum number of uncovered targets in one of the four possible directions. Our method deploys minimum number of sensors to cover all the targets with least number of iterations. It also provides full coverage and connectivity with minimum deployment cost.

Multi-Objective Solar Farm Design Based on Parabolic Collectors

The configuration of solar farms, in which solar collectors are arranged in rows, is related to field and collector characteristics and solar radiation data. The main parameters considered during the optimization of solar farm designs include the number of collector rows, the center-to-center distance between collectors, collector inclination angles, and the rim angles. Solar collectors can be subjected to shading depending on the spacing between the collector rows, collector height and angle, row length, and latitude of the solar field. This study aims to optimize solar farm design by ensuring the farm receives the maximum incident solar energy and incurs the minimum deployment cost. The proposed mathematical model for photovoltaic panels is presented in detail. A multi-objective evolutionary algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II), is used to achieve an optimum solar farm design that incorporates parabolic trough panels. The performances of the parabolic and flat panels are also compared, and the findings are discussed in detail. Based on the obtained results, we can verify that the parabolic PV model could generate more energy than the flat model. However, at the same cost, the flat PV model generated more energy than the parabolic model. There is a trade-off between the absolute values of the various objectives, and a solution can be selected based on the customer’s requirements and desires.

Minimum-cost deployment of adjustable readers to provide complete coverage of tags in RFID systems

Abstract In Internet of things (IoT), radio frequency identification (RFID) plays an important role to help people rapidly obtain information of objects associated with tags. Passive tags are cheap and require no batteries to operate, so they are widely used in RFID applications. Readers, on the other hand, have to provide power to activate passive tags to get their data. However, collision occurs when two readers send signals to a tag at the same time. Therefore, it is critical to decide the locations of readers, namely reader deployment, to avoid collision. This paper considers adjustable readers, whose transmitted power is configurable to provide different communication range, and proposes a minimum-cost RFID reader deployment (MR2D) problem. Given the positions of tags, it determines how to deploy readers and adjust their transmitted power to cover all tags, such that we can use the minimum number of readers and save their energy. To facilitate data transmission and reduce hardware cost, we restrict the number of tags that each reader can cover and allow readers to have few overlapped tags in their communication range. Then, we develop an efficient solution to the MR2D problem by clustering tags into groups and placing a reader to cover each group to meet the above conditions. Simulation results show that our proposed solution not only saves the number of RFID readers but also reduces their energy consumption, as compared with existing methods.

Minimum Cost Deployment of Heterogeneous Directional Sensor Networks for Differentiated Target Coverage

A basic deployment goal of a directional sensor network (DSN) is to satisfy the coverage quality requirements of targets. The limited sensing angle of directional sensors makes the deployment of such a network more complicated than that of an omnidirectional sensor network. In some emerging DSN applications, targets may have differentiated coverage quality requirements due to their differentiated importance or priorities. Meanwhile, directional sensors of multiple types with different sensing ranges and prices are available for optimally organizing a network. To deploy such a DSN with satisfied network connectivity under the minimum cost constraint is a significant problem to be solved. This work formulates and addresses the problem for the first time. First, we prove its NP-completeness. Then, this problem is formulated as an integer linear programming model. To solve it, we propose three algorithms, i.e., a greedy heuristic, local search, and particle swarm optimization. Extensive numerical experiments are conducted to validate the proposed algorithms. The results show that all the proposed algorithms can effectively solve the problem, and the last one can achieve both highest success rate and best solution quality.

A Review on Sensor Node Placement Techniques in Wireless Sensor Networks

One way to provide Wireless Sensor Network (WSN) with maximum coverage, maximum connectivity, minimum deployment cost and minimum energy consumption is through an effective planning mechanism in arranging an optimum number of sensor nodes. Proper planning will provide a cost-effective deployment by having optimal placements for the sensor nodes. Sensor node placement schemes are needed to accommodate the balance of coverage and energy consumption since closer sensor nodes not only reduces the energy consumption but will result in the network coverage becoming smaller. This paper critically reviews the research and development work done in sensor node placement. Based on the review, the design objectives that need to be considered are identified. Most of the work reviewed focused on two or three design objectives.