1,208 research outputs found
A genetic algorithm-based approach to optimize the coverage and the localization in the wireless audiosensors networks
International audienceCoverage is one of the most important performance metrics for sensor networks that reflects how well a sensor field is monitored. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN in order to maximize the coverage problem and to optimize the audio localization in wireless sensor networks. First, we introduce the problem of deployment. Then we propose a mathematical formulation and a genetic based approach to solve this problem. Finally, we present the results of experimentations. This paper presents a genetic algorithm which aims at searching for an optimal or near optimal solution to the coverage holes problem. Compared with random deployment as well as existing methods, our genetic algorithm shows significant performance improvement in terms of quality
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Acoustic Sensor Planning for Gunshot Location in National Parks: A Pareto Front Approach
In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabaneros National Park and the SEPRONA service, proposes a system to automatically detect and locate gunshots. This work presents its technical aspects related to network design and planning. The system consists of a network of acoustic sensors that locate gunshots by hyperbolic multi-lateration estimation. The differences in sound time arrivals allow the computation of a low error estimator of gunshot location. The accuracy of this method depends on tight sensor clock synchronization, which an ad-hoc time synchronization protocol provides. On the other hand, since the areas under surveillance are wide, and electric power is scarce, it is necessary to maximize detection coverage and minimize system cost at the same time. Therefore, sensor network planning has two targets, i.e., coverage and cost. We model planning as an unconstrained problem with two objective functions. We determine a set of candidate solutions of interest by combining a derivative-free descent method we have recently proposed with a Pareto front approach. The results are clearly superior to random seeding in a realistic simulation scenario
Coverage and Energy Efficiency Optimization for Randomly Deployed Multi-Tier Wireless Multimedia Sensor Networks
In this study, a novel multi-tier framework is proposed for randomly deployed WMSNs. Low cost directional Passive Infrared Sensors (PIR sensors) are randomly deployed across a Region of Interest (RoI), which are activated according to the Differential Evolution (DE) algorithm proposed for coverage optimization. The proposed DE and the Genetic Algorithms are applied to optimize the coverage maximization using minimum sensors. Results obtained using the two approaches are tested and compared. Only the scalar sensors that are yielded by the coverage optimization process are kept active throughout the network lifetime while the multimedia sensors are kept in silent. When an event is detected by a scalar sensor, the corresponding multimedia sensor(s), in whose effective coverage field of view (FoV) that the target falls, is then activated to capture the event (target point/scene). The analysis of the network total energy expenditure and a comparison of the proposed framework to current approaches and frameworks is made. Simulation results show that the proposed architecture achieves a remarkable network lifetime prolongation while extending the coverage area
Dynamic Reconfiguration in Camera Networks: A Short Survey
There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration
Design of an Optimal Testbed for Tracking of Tagged Marine Megafauna
Underwater acoustic technologies are a key component for exploring the
behavior of marine megafauna such as sea turtles, sharks, and seals. The
animals are marked with acoustic devices (tags) that periodically emit signals
encoding the device's ID along with sensor data such as depth, temperature, or
the dominant acceleration axis - data that is collected by a network of
deployed receivers. In this work, we aim to optimize the locations of receivers
for best tracking of acoustically tagged marine megafauna. The outcomes of such
tracking allows the evaluation of the animals' motion patterns, their hours of
activity, and their social interactions. In particular, we focus on how to
determine the receivers' deployment positions to maximize the coverage area in
which the tagged animals can be tracked. For example, an overly-condensed
deployment may not allow accurate tracking, whereas a sparse one, may lead to a
small coverage area due to too few detections. We formalize the question of
where to best deploy the receivers as a non-convex constraint optimization
problem that takes into account the local environment and the specifications of
the tags, and offer a sub-optimal, low-complexity solution that can be applied
to large testbeds. Numerical investigation for three stimulated sea
environments shows that our proposed method is able to increase the
localization coverage area by 30%, and results from a test case experiment
demonstrate similar performance in a real sea environment. We share the
implementation of our work to help researchers set up their own acoustic
observatory.Comment: Submitted for publication in Frontiers in Marine Science, special
topic on Tracking Marine Megafauna for Conservation and Marine Spatial
Plannin
A generalized model for indoor location estimation using environmental sound from human activity recognition
The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (eg, hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct
Beyond 5G URLLC Evolution: New Service Modes and Practical Considerations
Ultra-reliable low latency communications (URLLC) arose to serve industrial
IoT (IIoT) use cases within the 5G. Currently, it has inherent limitations to
support future services. Based on state-of-the-art research and practical
deployment experience, in this article, we introduce and advocate for three
variants: broadband, scalable and extreme URLLC. We discuss use cases and key
performance indicators and identify technology enablers for the new service
modes. We bring practical considerations from the IIoT testbed and provide an
outlook toward some new research directions.Comment: Submitted to IEEE Wireless Commun. Ma
Effective Node Clustering and Data Dissemination In Large-Scale Wireless Sensor Networks
The denseness and random distribution of large-scale WSNs makes it quite difficult to replace or recharge nodes. Energy efficiency and management is a major design goal in these networks. In addition, reliability and scalability are two other major goals that have been identified by researchers as necessary in order to further expand the deployment of such networks for their use in various applications. This thesis aims to provide an energy efficient and effective node clustering and data dissemination algorithm in large-scale wireless sensor networks. In the area of clustering, the proposed research prolongs the lifetime of the network by saving energy through the use of node ranking to elect cluster heads, contrary to other existing cluster-based work that selects a random node or the node with the highest energy at a particular time instance as the new cluster head. Moreover, a global knowledge strategy is used to maintain a level of universal awareness of existing nodes in the subject area and to avoid the problem of disconnected or forgotten nodes. In the area of data dissemination, the aim of this research is to effectively manage the data collection by developing an efficient data collection scheme using a ferry node and applying a selective duty cycle strategy to the sensor nodes. Depending on the application, mobile ferries can be used for collecting data in a WSN, especially those that are large in scale, with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry-based approach thus eliminates, or minimizes, the need for the multi-hop forwarding of data, and as a result, energy consumption at the nodes will be significantly reduced. This is especially true for nodes that are near the base station as they are used by other nodes to forward data to the base station. MATLAB is used to design, simulate and evaluate the proposed work against the work that has already been done by others by using various performance criteria
Towards UAV Assisted 5G Public Safety Network
Ensuring ubiquitous mission-critical public safety communications (PSC) to all the first responders in the public safety network is crucial at an emergency site. The first responders heavily rely on mission-critical PSC to save lives, property, and national infrastructure during a natural or human-made emergency. The recent advancements in LTE/LTE-Advanced/5G mobile technologies supported by unmanned aerial vehicles (UAV) have great potential to revolutionize PSC.
However, limited spectrum allocation for LTE-based PSC demands improved channel capacity and spectral efficiency. An additional challenge in designing an LTE-based PSC network is achieving at least 95% coverage of the geographical area and human population with broadband rates. The coverage requirement and efficient spectrum use in the PSC network can be realized through the dense deployment of small cells (both terrestrial and aerial). However, there are several challenges with the dense deployment of small cells in an air-ground heterogeneous network (AG-HetNet). The main challenges which are addressed in this research work are integrating UAVs as both aerial user and aerial base-stations, mitigating inter-cell interference, capacity and coverage enhancements, and optimizing deployment locations of aerial base-stations.
First, LTE signals were investigated using NS-3 simulation and software-defined radio experiment to gain knowledge on the quality of service experienced by the user equipment (UE). Using this understanding, a two-tier LTE-Advanced AG-HetNet with macro base-stations and unmanned aerial base-stations (UABS) is designed, while considering time-domain inter-cell interference coordination techniques. We maximize the capacity of this AG-HetNet in case of a damaged PSC infrastructure by jointly optimizing the inter-cell interference parameters and UABS locations using a meta-heuristic genetic algorithm (GA) and the brute-force technique. Finally, considering the latest specifications in 3GPP, a more realistic three-tier LTE-Advanced AG-HetNet is proposed with macro base-stations, pico base-stations, and ground UEs as terrestrial nodes and UABS and aerial UEs as aerial nodes. Using meta-heuristic techniques such as GA and elitist harmony search algorithm based on the GA, the critical network elements such as energy efficiency, inter-cell interference parameters, and UABS locations are all jointly optimized to maximize the capacity and coverage of the AG-HetNet
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