Multi-objective Deployment Research Articles

Striking the perfect balance: Multi-objective optimization for minimizing deployment cost and maximizing coverage with Harmony Search

In the Internet of Things (IoT) era, wireless sensor networks play a critical role in communication systems. One of the most crucial problems in wireless sensor networks is the sensor deployment problem, which attempts to provide a strategy to place the sensors within the surveillance area so that two fundamental criteria of wireless sensor networks, coverage and connectivity, are guaranteed. In this paper, we look to solve the multi-objective deployment problem so that area coverage is maximized and the number of nodes used is minimized. Since Harmony Search is a simple yet suitable algorithm for our work, we propose Harmony Search algorithm along with various enhancement proposals, including heuristic initialization, random sampling of sensor types, weighted fitness evaluation, and using different components in the fitness function, to provide a solution to the problem of sensor deployment in a heterogeneous wireless sensor network where sensors have different sensing ranges. On top of that, the probabilistic sensing model is used to reflect how the sensors work realistically. We also provide the extension of our solution to 3D areas and propose a realistic 3D dataset to evaluate it. The simulation results show that the proposed algorithms solve the area coverage problem more efficiently than previous algorithms. Our best proposal demonstrates significant improvements in coverage ratio by 10.20% and cost saving by 27.65% compared to the best baseline in a large-scale evaluation.

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Multi-Objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage

Multi-Objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage

Multi-objective deployment model for regional air defense

Multi-objective deployment model for regional air defense

Hesitant fuzzy for conflicting criteria in multi-objective deployment of electric vehicle charging stations

The penetration rate of electric vehicles is expected to experience continual growth. The expansion planning of charging stations includes conflicting interests in urban distribution systems. This paper addresses three different viewpoints from stakeholders, municipalities, and DSOs who may not easily agree on unanimous interests in infrastructure developments. A multi-objective optimization approach is suggested regarding profit, greenhouse gas (GHG) emission, and voltage profile. The vagueness and contradiction of experts’ opinions are taken into account. Multi-Criteria Decision Making (MCDM) techniques are employed to prioritize the candidates. Besides, uncertainties of price, load, and number of available electric vehicles are considered and the best solutions are selected using Crow Search Algorithm (CSA). Hesitant Fuzzy Independent Judgement (HFIJ), Analytic Hierarchy Process (AHP), and Hesitant Fuzzy AHP mechanisms are implemented to vote for the best expansion solution. IEEE 69-bus test system with modified loads along with the market data from Nordpool are considered in simulations. Sensitivity analysis is also provided to assess the solidity of MCDM. The results show that HFAHP makes more concrete and consistent assembly choices that even 30% variations in weighting coefficients cannot change the alternative rankings.

Multiobjective Deployment of Data Analysis Operations in Heterogeneous IoT Infrastructure

The growth of Internet of Things (IoT) technology brings many new opportunities for applications in areas including smart healthcare, smart buildings, and smart agriculture. These applications must normally distribute the computations, required for extracting value from sensor data, over the IoT infrastructure platforms (e.g., sensors, phones, field-gateways, and clouds). This can be very challenging for IoT application developers due to the heterogeneity of the aforementioned platforms, potentially conflicting nonfunctional requirements (e.g., battery power, latency, and cost), and related deployment criteria, which is impossible to resolve manually. To address the above challenges, we have developed the PATH2iot framework that decomposes a complex IoT application into self-contained micro-operations. Based on the deployment criteria, PATH2iot automatically distributes the set of micro-operations across IoT infrastructure platforms, while respecting their run-time data and control flow dependencies. In our previous work, we have shown how to use the PATH2iot to optimize the battery life of a healthcare wearable. In this article, we describe a new research that significantly extends PATH2iot , which introduces a heuristic model capable of making optimal deployment decisions based on multiple conflicting nonfunctional requirements and selection criteria (user preferences). It does so by leveraging a well-known multicriteria decision-making method called the analytic hierarchical processes (AHP). The applicability of the deployment model is validated based on a real-world digital healthcare analytics use case. The results show that our model is able to find the optimal deployment solution for different user preferences.

3D Terrain Multiobjective Deployment Optimization of Heterogeneous Directional Sensor Networks in Security Monitoring

The traditional deployment research on wireless sensor networks (WSNs) has mainly focused on 2D plane and 3D full space; also, the sensors considered are almost always omni-directional sensors and are usually homogeneous. However, this type of research cannot fulfill the diverse requirements required for practical 3D environment. We study the deployment problem of heterogeneous directional sensor networks (HDSNs) on 3D terrain, which is more suitable for practical security monitoring requirements and has more practical significance. In this paper, we propose a novel uncertain comprehensive coverage model: a modified 3D directional sensing model is presented, and a non-probabilistic measure based fusion operator is utilized. We transform the deployment problem into a multiobjective optimization problem by comprehensively considering Coverage , Connectivity Uniformity and Deployment Cost , and we use various state-of-the-art multiobjective optimization algorithm based deployment approaches to address it. We conduct deployment experiments on three types of real-world 3D terrain data (plain, hill and mountain). Through the analysis of the deployment results, we obtain a deeper understanding and insight into the multiobjective deployment problem of HDSN. Meanwhile, we more clearly recognize the characteristics of the deployment approaches.

Multi-sensor multi-objective optimization deployment on complex terrain based on Pareto optimal theory

Multiple optimization objectives are often taken into account during the process of sensor deployment. Aiming at the problem of multi-sensor deployment in complex environment, a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed. First, the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity, and a truncation probability sensing model is presented. Two strategies, the local mutation operation and parameter adaptive operation, are introduced to improve the optimization ability of quantum particle swarm optimization (QPSO) algorithm, and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front. Then, considering the multi-objective deployment requirements, a novel multi-sensor deployment method based on the multi-objective optimization theory is built. Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once. Compared with the traditional algorithms, the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.

The multi-objective deployment optimization of UAV-mounted cache-enabled base stations

The deployment of unmanned aerial vehicle (UAV)-mounted base stations is emerging as an effective solution for providing wireless communication service to ground terminals (GTs) which have failed to be associated with ground base stations for some reason. Meanwhile, with the propose of reducing the transmission latency and easing the load of backhaul links between UAVs and the core network, UAVs are equipped with the ability of caching popular contents in the storage of base stations. In this paper, we investigate the efficient deployment problem of UAVs (such as transmitting power, number of UAVs, locations and caching) while guaranteeing the quality of service requirements. In this case, the UAV plays the role of a coordinator to provide high-quality communication service for GTs as well as maximize the benefit of caching. However, there exists an intractable issue that UAVs need to consider the optimization problem of multiple performance metrics with various types of optimization variables. To tackle the challenge, we propose a reinforcement learning-based approach to solve the multi-objective deployment problem while maintaining the optimal tradeoff between power consumption and backhaul saving. Numerical results evaluate the performance of the proposed algorithm.

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage , lifetime, and reliability . Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.

Optimizing the Multi-Objective Deployment Problem of Mlat System

Multilateration (MLAT) systems are powerful means for air traffic surveillance. These systems aim to extract, and display to air traffic controllers identification of aircrafts or vehicles equipped with a transponder. They provide an accurate and real-time data without human intervention using a number of ground receiving stations, placed in some strategic locations around the coverage area, and they are connected with a Central Processing Subsystem (CPS) to compute the target (i.e., aircraft or vehicle) position. The MLAT performance strongly depends on system layout design which consists on deploying the minimum number of stations, in order to obtain the requested system coverage and performance, meeting all the regulatory standards with a minimum cost. In general, choosing the number of stations and their locations to cope with all the requirements is not an obvious task and the system designer has to make several attempts, by trial and error, before obtaining a satisfactory spatial distribution of the stations. In this work we propose a new approach to solve the deployment of Mlat stations problem by focusing on the number of deployed stations and the coverage as the main objectives to optimize. The Non-dominated Sorting Genetic Algorithm II(NSGA-II) was used in order to minimize the total number of stations required to identify all targets in a given area, with the aim to minimize the deployment cost, accelerating processes, and achieve high availability and reliability. The proposed approach is more efficient and converge rapidly which makes it ideal for our research involving optimal deployment of Mlat station.

A Similarity-Based Multiobjective Evolutionary Algorithm for Deployment Optimization of Near Space Communication System

The deployment of the airships plays a key role in maximizing the performance of the near space communication system. The main problem is how to strike a balance between the conflicting network speed and coverage for complex user distribution. In this paper, we propose a multiobjective deployment optimization model considering path loss, user demand, and inner structure. Under the framework of the multiobjective evolutionary algorithm (MOEA) based on decomposition (MOEA/D), we propose a similarity-based MOEA to optimize this problem. The proposed algorithm is motivated by the population’s perception on the decision variable space. The proposed algorithm perceives the decision variable space by deploying airships to latent regions. The perceptions of different solutions are related by the similarity between their deployments and utilized differently by crossover and mutation. The proposed algorithm is tested on five designed problems compared with MOEA/D with the other popular reproduction operators. We also test the proposed scheme integrated with another two popular algorithms. The experimental results show that the similarity-based MOEA/D outperforms the other algorithms significantly in detecting hotspots, tracking multiple hotspots and safely deploying airships for most cases. The proposed scheme also works well with the other algorithms.

Multi-subpopulation evolutionary algorithms for coverage deployment of UAV-networks

The deployment of an unmanned aerial network (UAV-network) for the optimal coverage of ground nodes is an NP-hard problem. This work focuses on the application of a multi-layout multi-subpopulation genetic algorithm (MLMPGA) to solve multi-objective coverage problems of UAV-networks. The multi-objective deployment is based on a weighted fitness function that takes into account coverage, fault-tolerance, and redundancy as relevant factors to optimally place the UAVs. The proposed approach takes advantage of different subpopulations evolving with different layouts. This feature is aimed at reflecting the evolutionary concept of different species adapting to the search space conditions of the multi-objective coverage problem better than single-population genetic algorithms. The proposed multi-subpopulation genetic algorithm is evaluated and compared against single-population genetic algorithm configurations and other well-known meta-heuristic optimization algorithms, such as particle swarm optimization and hill climbing algorithm, under different numbers of ground nodes. The proposed MLMPGA achieves significantly better performance results than the other meta-heuristic algorithms, such as classical genetic algorithms, hill climbing algorithm, and particle swarm optimization, in the vast majority of our simulation scenarios.

Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints

A wireless sensor network (WSN) deployment requires the identification of optimal network nodes (sensor and sink) positions in an area of interest, to ensure the best network performances (Senouci et al. in Smart Communications in Network Technologies (SaCoNeT), 2014 International Conference on, IEEE, pp 1---6, 43). The deployment process can be divided in two main parts: (1) WSN model construction, and (2) placement optimization. Few research works were interested by WSN deployment in indoor environment, even though, most of them consider the objectives (coverage, cost, connectivity) individually without considering the sensors and sink in the same time. This paper proposes a multi-objective deployment strategy (MODS), where all important objectives are integrated. The MODS uses the multi-objective evolutionary algorithms to get near optimal solution for WSN deployment problem. An original coding solution, integrating both network cost and nodes positions is proposed. A comparative study between two evolutionary strategies (classical GA, and NSGA-II) was performed to identify the use case of each one. Obtained results showed the interest of the proposed methodology.

A Survey of Multi-Objective Deployment in Wireless Sensor Networks

The major challenge in designing wireless sensor networks (WSNs) is to find tradeoff between the desired and contrary requirements for the lifetime, coverage or cost while coping with the computation, energy and communication constraints. This paper examines the optimal placement of nodes for a WSN. It is impossible to consider the deployment of the nodes separately from WSNs applications. We highlight the properties of WSNs applications that determine the placement problem. We identify and enumerate the various objectives that should be considered. The paper provides an overview and concentrates on multi-objective strategies, their assumptions, optimization problem formulation and results.

Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints

In this paper, we address a wireless sensor network deployment problem. It is considered when the deployment field is characterized by a geographical irregularity of the monitored event. Each point in the deployment area requires a specific minimum guarantee of event detection probability. Our objective is to generate the best network topology while minimizing cost of deployment, ensuring quality of monitoring and network connectivity, and optimizing network lifetime. The problem is formulated as combinatorial optimization problem, which is NP-complete. Unfortunately, due to the large solution state space and the exponential computational complexity, the exact methods can be applied only in the case of small-scale problem. To overcome the complexity of an optimal resolution, we propose new scalable deployment heuristics based on artificial potential field and Tabu search metaheuristic, namely potential field deployment algorithm ( PFDA) and multi-objective deployment algorithm ( MODA). We compare our proposal to the related deployment strategies, the obtained results show that PFDA and MODA obtain the best performances.

A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks

A Wireless Sensor Network (WSN) design often requires the decision of optimal locations (deployment) and transmit power levels (power assignment) of the sensors to be deployed in an area of interest. Few attempts have been made on optimizing both decision variables for maximizing the network coverage and lifetime objectives, even though, most of the latter studies consider the two objectives individually. This paper defines the multiobjective Deployment and Power Assignment Problem (DPAP). Using the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), the DPAP is decomposed into a set of scalar subproblems that are classified based on their objective preference and tackled in parallel by using neighborhood information and problem-specific evolutionary operators, in a single run. The proposed operators adapt to the requirements and objective preferences of each subproblem dynamically during the evolution, resulting in significant improvements on the overall performance of MOEA/D. Simulation results have shown the superiority of the problem-specific MOEA/D against the NSGA-II in several network instances, providing a diverse set of high quality network designs to facilitate the decision maker's choice.