Ubiquitous sensor network simulation and emulation environments: A survey

Sharif, Mohammad; Sadeghi‐Niaraki, Abolghasem

doi:10.1016/j.jnca.2017.05.009

Cited by 43 publications

(24 citation statements)

References 151 publications

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“…Both users' and sensors' participations can be classified as physical, virtual, and logical forms. The physical form explicitly captures information by manual input from users (e.g., filling out questionnaires) or from deployed hardware sensors 56 (e.g., Wearable gadgets) that are capable of monitoring, sensing, and/or tracking any physical data. The virtual form implicitly relies on collecting information from software applications (e.g., social media networks) or services (e.g., browsing passengers' movements by e-ticket information).…”

Section: Context Acquisitionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Data Mining Knowl Discov 2018Discov , 8:e1233. doi: 10.1002Discov /widm.1233 INTRODUCTIONM ovement is intrinsically a continuous phenomenon and is normally recorded as discrete snapshots at different temporal resolutions. The three fundamental sets pertinent to movement are space S (i.e., set of locations/places), time T (i.e., set of instants/intervals), and object O (i.e., set of entities). 1 In this paper, by movement we mean the change in the spatial location of one or more entities (e.g., people, animals, and vehicles) over time, not a change in entities' geometries (e.g., an individual's body parts). Such changes can be represented as a function μ: T ! S, which maps any time to a location in space. The purpose of analyzing movement data in the majority of research fields (e.g., geography, ecology, sociology, and transportation) has focused on the development of insights into the behavior of moving entities. However, the movement behavior of entities is generally associated with their internal states and external factors. 2 In other words, understanding entities' movement greatly depends on recognizing the characteristics attributed to the entity itself (e.g., the entities' purposes and intentions for movement, the entities' perceptions of space) as well as the discovery of geographical circumstances during the movement (e.g., weather conditions and traffic). We, respectively, term these influential variables internal and external contexts in this paper. of 19Because of the progress in sensing, imaging, tracking, and navigation technologies and infrastructures in recent years, unprecedented amount...

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Section: Context Acquisitionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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“…WSN, a set of spatially distributed sensor nodes, is deployed in a geospatial space to detect and monitor different phenomena [ 73 , 74 , 75 ]. The robustness of WSN in collecting and transmitting data is affected by the sensor limitations including sensing range, communication, power supply, and computational capacities.…”

Section: Health and Environment Sensor Network Deploymentmentioning

confidence: 99%

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Shakeri

Sadeghi‐Niaraki

Choi

et al. 2020

Sensors

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With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

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“…Designing analytical models and implementing them in simulation tools is standard practice in the wireless networking field in order to accelerate the evaluation of new protocols and mechanisms. Indeed, network simulation has been largely studied in the case of wireless communication [12]. Nonetheless, VLC performance evaluation remains poorly investigated and VLC simulation tools are still missing.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of LED-to-Camera Communications

Duque¹,

Stanica

Desportes³

et al. 2019

Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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The use of LED-to-camera communication opens the door to a wide range of use cases and applications, with diverse requirements in terms of quality of service. However, while analytical models and simulation tools exist for all the major radio communication technologies, the only way of currently evaluating the performance of a network mechanism over LED-to-camera is to implement and test it. Our work aims to fill this gap by proposing a Markov-modulated Bernoulli process to model the wireless channel in LED-to-camera communications, which is shown to closely match experimental results. Based on this model, we develop and validate CamComSim, the first network simulator for LED-to-camera communications.

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Ubiquitous sensor network simulation and emulation environments: A survey

Cited by 43 publications

References 151 publications

Context‐aware movement analytics: implications, taxonomy, and design framework

Context‐aware movement analytics: implications, taxonomy, and design framework

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Performance Evaluation of LED-to-Camera Communications

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