Maximizing Network Coverage Research Articles

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

A comparative analysis of alpha-beta-gamma and close-in path loss models based on measured data for 5G mobile networks

Mobile coverage is crucial for the fifth-generation (5G) network since it affects the network's accessibility and dependability in various locations. With a wider coverage, more people and devices will access the 5G network, allowing them to experience the benefits and capabilities of this important technology. However, the development and construction of the networks need a lot of time and effort to maximize network coverage and service delivery while utilizing the fewest possible infrastructure components. Path loss models are commonly employed to predict network coverage. Therefore, it is expedient to adopt a path loss model that is suitable for the specified geographical area. In this paper, we investigated and analysed two empirical models: the Close-in free space reference distance (CI) model and the alpha-beta-gamma (ABG) model at the low- and mid-spectrum bands. Secondly, the path loss parameters were derived to characterize the two empirical models based on data from the measurement campaign. Finally, Root Mean Square Error (RMSE) was employed for the comparison of the models. It was found that at frequencies 0.9 and 1.8 GHz, the CI model's least RMSE values were 3.6 and 4.1 at location 1 and 4.7 and 4.2 dB at location 2, respectively, outperforming the ABG. However, at 3.4 GHz, the ABG with 3.25 and 1.75 dB outperformed the CI at both locations. For future work, the integration of machine learning techniques to dynamically predict and adapt path loss models in real time based on continuous data collection will be appropriate.

Low discrepancy on non-linear sensor deployment in a time-critical linear IIoT network

Linear networks are characterized by their long and narrow topology, which transmits data in a linear manner. For time-critical linear IIoT networks, stable connectivity is paramount. A good deployment strategy ensures stable connectivity in the network and maximize the lifetime and coverage of the network. In order to achieve this goal, deployment strategies should address tunnelling effects in linear networks. The proposed method tackles three major issues: 1. minimizing tunnelling effects using non-linear sensor deployment method, 2. maximizing network coverage by introducing a modified Latin Hypercube Sequence (LHS), and 3. Minimizing data interference in the multichannel TSCH network using a neighbour count-based sensor node deployment technique. The simulation validates the efficacy of the suggested approach in prolonging the network’s lifespan by mitigating the tunnelling effect near the sink. Moreover, it optimally utilizes over 70% of the mean energy within the network system. This is a significant improvement over previous algorithms which often failed to even utilizing 40% of the energy, and many a times much lesser. In terms of network coverage, it surpasses other algorithms. Consequently, the proposed method delivers economic advantages by extending the network’s longevity, achieved through a higher number of nodes concentrated around the sink, and significantly reduces the tunnelling effect in the network.

A Novel Coverage Optimization Scheme Based on Enhanced Marine Predator Algorithm for Urban Sensing Systems

Ensuring the efficient operation of urban sensing systems under emergency conditions represents a current and significant challenge. To address the typical problem of low network coverage due to random deployment of wireless sensor network nodes in urban sensing systems, a novel coverage optimization scheme with an enhanced Marine Predator Algorithm (EMPA) is proposed. For a given target network monitoring area, a joint node sensing model based on Boolean sensing is constructed with the environmental information of the monitoring area as input, and then the model is optimized using EMPA for several iterations to obtain the optimized maximum network coverage and its corresponding optimal node location deployment scheme. Based on the standard MPA, firstly, the Tent chaotic map is introduced to initialize the population to maintain the population diversity of the algorithm; secondly, the adaptive jump step factor is introduced in the jump phase to improve the ability of the algorithm to jump out of the local optimum; finally, the double-subgroup location update strategy is introduced before the end of each iteration of the algorithm, which can better balance the local and global search and further enhance the algorithm’s optimal search capability. The algorithm is compared with four unimodal and multimodal benchmark test functions and applied to three network coverage optimization scenarios in urban sensing systems. The results show that the EMPA algorithm can efficiently optimize the network nodes’ uniform distribution, enhance average network coverage, and save a certain level of node deployment costs for urban sensing systems.

Multimodal Public Transit Network Design Method Based on Hub-and-Spoke Infrastructure

This paper presents a two-phase method for generating a hub-and-spoke network for a multimodal public transit network (MPTN). The first phase addresses the problem of clustering in MPTNs, which is a key operation in solving the hub location problem (HLP) in MPTN design. However, previous work often oversimplifies or neglects the clustering phase, so this study presents a spatial clustering algorithm that can process real urban MPTNs without reducing their size or dividing them into zones. The algorithm is tested on four large city datasets and compared with popular spatial clustering algorithms. In the second phase, a heuristic algorithm is presented that aims to maximize network coverage. The proposed method is tested using the MPTN of Greater London and the resulting hub-and-spoke network is compared with the Journey API provided by Transport for London. The results show that the total travel time is improved by 16.9% with a strict hubbing policy. Transportation planners can adapt the proposed method to their specific needs by changing the size and number of clusters using the cluster scaling factor ([Formula: see text]) parameter and including or excluding any criteria in the decision equations.

Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance. The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’ access network. The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness. Coverage and energy usage are mostly determined by successful sensor placement strategies. Nature-inspired algorithms are the most effective solution for short sensor lifetime. The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks (WSNs’) maximum network coverage. Moreover, it identifies quantity of installed sensor nodes for the given area. Superiority of algorithm has been identified based on value of optimized energy. The first half of the paper’s literature on nature-inspired algorithms is discussed. Later six metaheuristics algorithms (Grey wolf, Ant lion, Dragonfly, Whale, Moth flame, Sine cosine optimizer) are compared for optimal coverage of WSNs. The simulation outcomes confirm that whale optimization algorithm (WOA) gives optimized Energy with improved network coverage with the least number of nodes. This comparison will be helpful for researchers who will use WSNs in their applications.

Robust goal programming approach for healthcare network management for perishable products under disruption

In this paper, a robust goal programming model for a healthcare network design problem under disruption is developed. The objective of the proposed multi-period, multi-echelon model is to simultaneously optimize the total deviation from the considered three goals, including minimizing network cost, maximizing network coverage, and maximizing network reliability. Distribution uncertainty of healthcare demand and facility capacity is captured. The perishability of healthcare products is regarded with limited shelf-life. Using an option contract ensures appropriate coordination with suppliers during pre and post-disaster periods. Via handling the tractable robust budget-based counterpart model, suitable strategies are proposed to present the designed network's efficiency and effectiveness. Risk mitigation strategies deal with disrupted demand and supply in the healthcare network with specific impacts on coverage, cost, and reliability of the services. The proposed cooperate coverage mechanism provides appropriate service coverage under disruption propagation. Finally, the option contract has achieved cost-efficient coordination under demand uncertainty. Furthermore, practical managerial insights are demonstrated by investigating a real-life case study and conducting extensive sensitivity analyses.

Multistrategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization

Achieving maximum network coverage with a limited number of sensor nodes is key to node deployment of wireless sensor network (WSN). This paper proposes an improved marine predator algorithm (IMPA) for 3D surface wireless sensor network deployment. A population evolution strategy based on random opposition-based learning and differential evolution operator is proposed to enrich the population diversity and improve the global search capability of the algorithm. The grouping idea of the Shuffled Frog Leaping Algorithm (SFLA) is then introduced. A local search strategy based on the SFLA is proposed to replace the FADs effect of MPA and enhance the ability of the algorithm to escape from the local optimum. A quasireflected opposition-based learning strategy is also presented to improve the optimization accuracy, accelerate the convergence speed of the algorithm, and improve the quality of the solution. Fifteen benchmark functions are selected for testing. The results are compared with seven different algorithms. The results show that the improved algorithm has excellent optimization performances. Finally, the IMPA is applied to optimize WSN coverage on 3D surfaces. The experimental results show that the proposed IMPA has good terrain adaptation and optimal deployment capabilities. It can improve the coverage of the network, reduce the deployment cost, and extend the network life cycle.

The node deployment of wireless sensor networks based on mobile edge computing

When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J.

Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory

Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time.

Node Embedding for Security-Aware Clustering of Mobile Information-Centric Sensor Networks

In cluster-based information-centric wireless sensor networks (ICWSNs), mobile sensor nodes are grouped into clusters in rounds. In each cluster, a cluster head (CH) is selected, which collects, aggregates, and forwards locally sensed data to a sink node. CHs further store a copy of data for the round period to act as cache nodes and deliver data to mobile users upon requests. Nevertheless, clustering and securing mobile ICWSNs are challenging. This is because, in addition to sensor nodes’ and users’ mobility, sensor nodes are often resource constrained. Therefore, clustering and security resource allocation in mobile ICWSNs should be carefully redesigned to ensure efficient ICWSN operation, data security, and timely data access to mobile users. This article proposes a node embedding with security resource allocation (NESRA) clustering algorithm for mobile ICWSNs in rounds. NESRA allocates security resources to sensor nodes based on the location, mobility, and energy resources available in the first step. An optimization problem is formulated to select CHs that maximize network coverage and minimize data delivery delay to mobile users in the second step. In the third step, NESRA utilizes network representation learning that embeds sensor nodes’ location, mobility, and expected energy expenditure features into a 2-D space to form well-separated clusters of sensing nodes. Compared to existing works, NESRA achieves lower energy consumption, nodes’ death rate, and latency and allows higher throughput and cache nodes’ utilization with stable data security. Still, NESRA has some challenges to overcome in high-mobility networks.

Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks

Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.

Area Coverage Maximization under Connectivity Constraint in Wireless Sensor Networks.

Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors.

Energy Efficient Node Deployment Technique for Heterogeneous Wireless Sensor Network based Object Detection

Lifetime of the network and the quality of operation are the two important issues in a wireless sensor network system meant for object detection and tracking application. At the same time, there should be a tradeoff between network cost and the quality of operation as high cost of the network limits its real-time usability. Heterogeneous wireless sensor networks promises the prolonged network lifetime as well as enhances network reliability as they contain a mixture of nodes with different characteristics. Further prolongation of network lifetime can be achieved by managing the available node energy in a proper way, i.e, by minimizing number of communication, minimizing node density, minimizing overhead information generated during operation etc. Proper node deployment scheme not only helps to enhance the lifetime of the network but also helps in reducing deployment cost while maintaining the quality of operation in terms of object detection accuracy. This paper focuses on the energy efficient node deployment in heterogeneous wireless sensor network system with the features of maximum network coverage, optimum node density and optimum network cost. This paper proposes a novel energy efficient node deployment algorithm that determines the number of static and mobile nodes required for deployment and then relocates the mobile nodes to cover up the coverage hole using 8-neighbourhood and Particle Swarm Optimization (PSO) algorithm. The performance of the proposed algorithm is compared with corresponding model of Harmony Search Algorithm (HSA) and PSO based node deployment and it is seen that the proposed model outperforms better in comparison to them.

Application of the hill climbing algorithm to public transportation routes design in grid networks

Transit Routes Design (TRD) problem deals with optimizing the configuration of transit routes to satisfy a given objective, such as maximizing network coverage, while holding the budget constraint. In its discrete form, TRD is recognized as a computationally interactive problem. A review of the literature reveals that, despite extensive research on this problem, the number of studies on specific urban network configurations has remained limited. Among these studies, many have applied simplifying assumptions such as continuous design variables which may not be applicable to real-world settings. The present study focuses on a discrete version of the TRD problem for an urban “grid” network and aims to maximize the service coverage through the network. To this end, a local search Hill-Climbing (HC) heuristic algorithm is proposed and evaluated. The proposed HC algorithm performs several replications in which it starts with a combination of randomly selected routes and iteratively improves them by moving to the “best neighbor” until it reaches a local optimum solution. Our results for a 6×10 grid network for three budget levels (i.e. low, medium, and high budget levels) indicate that, in much shorter run-times than the exact algorithm, the proposed HC algorithm can produce high-quality solutions with 0.12%, 4.16%, and 2.22% difference from global optimums.

Optimal location of accident relief facilities in a railway network

We study the strategic problem of optimally locating different types of relief facilities across the railway network that could respond to an accident-related emergency. A multi-objective optimization program that aims to maximize network coverage, prioritizes important rail links, optimizes redundancy, and minimizes overall response times is proposed. The concept of cooperative coverage is adopted where a decaying function is used to ascertain the coverage, and the importance of rail-links is determined based on inputs from the experts via a questionnaire-based survey. The multi-objective optimization program is solved using an interactive augmented ε-constraint method that makes use of intervention from the decision-maker. The proposed optimization program is used to study the North Central Railway zone of Indian Railways, and the computational experiments demonstrate that reconfiguring the existing network of relief facilities will increase the coverage of rail nodes by 7.86% and that of rail-links by 10.78%.

Joint Deployment Strategy of Battery-Free Sensor Networks with Coverage Guarantee

The energy limitation of wireless sensors limits the lifetime of the traditional wireless sensor networks. The <b>Battery-Free Sensor Network (BF-WSN)</b> is a new network architecture proposed in recent years to address the limitation of wireless sensor networks. In a BF-WSN, the battery-free node can harvest energy from the ambient environment, and thus the lifetime of a BF-WSN is unlimited in terms of energy. The coverage quality is an important measurement of BF-WSNs. Considering the specific features of BF-WSNs, we propose a new deployment concept for BF-WSNs, named <i>Joint Deployment</i>. It aims to determine the locations and working schedules of sensor nodes to maximize network coverage quality. Based on the joint deployment concept, we propose a new deployment problem of battery-free sensor nodes. We prove that this problem is at least NP-Hard. We also analyze the upper bound of this problem. Furthermore, we propose an approximated algorithm to solve this problem and analyze the time complexity and the ratio bound of the algorithm. Extensive simulations are carried out to examine the performance of the proposed algorithm. The simulation results show that the algorithm is efficient and effective.

Optimization of Facility Location-Allocation Model for Base Tranceiver Station Antenna Establishment Based on Genetic Al-gorithm Considering Network Effectiveness Criteria (Case Study North of Kermanshah)

It is clear that the location of the BTS antennas plays a very important role in the proper service and coverage of the mobile connection. Proper location of these antennas is a major challenge for operators in each country, as in addition to maximum network coverage, service costs must also be acceptable and competitive. This means that in busy areas, in order to provide better service, the number of antennas must be greater and closer to each other. In general, the location problem is a type of optimization problem that aims to select a subset of the set of candidate locations to create the facilities that provide the best service at the lowest cost. To solve such problems in a reasonable time, we can use meta-heuristic algorithms to find solutions that are very close to the optimal solution. Accordingly, this paper attempts to apply the genetic algorithm to find a suitable solution for finding BTS mobile antennas in north Kermanshah. A GA model is proposed that improves the location coordinates of the current BTS antennas extracted from the GIS system. Comparison of model results with the status of BTS active antennas in Kermanshah shows the performance of the model.

OPTIMIZATION OF PLATE RECOGNITION SENSOR LOCATIONS: A CASE STUDY IN TURKEY

With the advances in sensor and data transfer technologies, the usage areas of Automatic License Plate Recognition (ALPR) systems have been expanded in the public and private sectors. In public safety, ALPR systems are used to monitor and control traffic data at both individual and collective levels. To build an efficient sensor network, the locations of ALPR systems should be determined optimally. This study provides an approach to determine optimal locations of ALPR systems that maximize network coverage consisting of two measures: i) vehicle coverage and ii) road coverage. The former represents the daily average vehicle flow whereas the latter stands for the number of road-links covered. The relative importance of vehicle and road coverages are taken into consideration, and optimal solutions under various scenarios are presented. A close neighbor constraint is introduced to avoid inefficient distribution of ALPR systems on the network. A case study with numerical examples designed for two cities in Turkey is provided. The centralized and decentralized solutions are compared against the current state, and the results show that the network coverage increases substantially in the centralized case.

AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is a mobile and wireless network that consists of connected vehicles, and stationary nodes called roadside units (RSUs) placed on the aboard of roads to improve traffic safety and to ensure drivers’ and passengers’ comfort. However, deploying RSUs is one of the most important challenges in VANETs due to the involved placement, configuration, and maintenance costs in addition to the network connectivity. This study focuses on the issue of deploying a set of RSUs that is able to maximize network coverage with a reduced cost. In this paper, we propose a new formulation of RSUs deployment issue as a maximum intersection coverage problem through a graph-based modeling. Moreover, we propose a new bio-inspired RSU placement system called Ant colony optimization system for RSU deployment in VANET (AC-RDV). AC-RDV is based on the idea of placing RSUs within the more popular road intersections, which are close to popular places like touristic and commercial areas. Since RSU deployment problem is considered as NP-Hard, AC-RDV inspires by the foraging behavior of real ant colonies to discover the minimum number of RSU intersections that ensures the maximum network connectivity. After a set of simulations and comparisons against traditional RSU placement strategies, the results obtained showed the effectiveness of the proposed AC-RDV in terms of number of RSUs placed, the average area coverage, the average connectivity and the overlapping ratio.