12 research outputs found
Study of Reliable Data Communication in Wireless Sensor Networks
A distributed wireless sensor network consists of numerous tiny autonomous sensing nodes deployed across a wide geographical area. These sensor nodes self organize and establish radio communication links with the neighboring nodes to form multi-hop routing paths to the central base station. The dynamic and lossy nature of wireless communication poses several challenges in reliable transfer of data from the sensor nodes to the sink. There are several applications of sensor networks wherein the data collected by the sensors in the network are critical and hence have to be reliably transported to the sink. An example of such an application is sensors with RFID readers mounted on them to read tag information from the objects in a factory warehouse. Here, the tag information recorded by the RFID reader is a critical piece of information which may not be available at a later point of time and hence has to be reliably transported to the sink. We study the various issues and analyze the design choices proposed in literature in addressing the challenge of sensors-to-sink reliable data communication in such applications. A cross-layer based protocol with MAC layer retransmissions and NACK (Negative Acknowledgment) based rerouting of data packets is developed to overcome link failures and provide reliability. The protocol is implemented on TinyOS and the performance of NACK based rerouting protocol in terms of percentage successful message reception is compared with NACK based retransmission protocol by running simulations on TOSSIM. The NACK based rerouting protocol provides greater reliability under different metrics like varying network size, network traffic and percentage of failed links in the network
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Grouping and sponsoring centric green coverage model for Internet of Things
Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network
Transport protocol for a real-time communication in wireless sensor actor networks - WSAN
Definitions and the basic concepts -- Real-time communication in sensor netwroks -- Real-time communication requirements -- Layer two achievements in real time communications -- Routing Protocols for sensor networks -- Transport layer -- Protocvol design -- Local time transport entity -- Sensor networks -- Geometry considerations -- The model -- The Protocol mechanisms -- Analytical model -- Travel time analysis -- Throughput analysis -- Mathematical model
5G Multi-access Edge Computing: Security, Dependability, and Performance
The main innovation of the Fifth Generation (5G) of mobile networks is the
ability to provide novel services with new and stricter requirements. One of
the technologies that enable the new 5G services is the Multi-access Edge
Computing (MEC). MEC is a system composed of multiple devices with computing
and storage capabilities that are deployed at the edge of the network, i.e.,
close to the end users. MEC reduces latency and enables contextual information
and real-time awareness of the local environment. MEC also allows cloud
offloading and the reduction of traffic congestion. Performance is not the only
requirement that the new 5G services have. New mission-critical applications
also require high security and dependability. These three aspects (security,
dependability, and performance) are rarely addressed together. This survey
fills this gap and presents 5G MEC by addressing all these three aspects.
First, we overview the background knowledge on MEC by referring to the current
standardization efforts. Second, we individually present each aspect by
introducing the related taxonomy (important for the not expert on the aspect),
the state of the art, and the challenges on 5G MEC. Finally, we discuss the
challenges of jointly addressing the three aspects.Comment: 33 pages, 11 figures, 15 tables. This paper is under review at IEEE
Communications Surveys & Tutorials. Copyright IEEE 202
Urban Activity Patterns Mining in Wi-Fi Access Point Logs
RÉSUMÉ Aujourd’hui la grande majorité des données sont basée sur des enquêtes ou des études appliquées
à des échantillons définis de la population. De plus les méthodes traditionnelles de collecte de données en termes de coûts ainsi que de temps tout en ne garantissant pas la représentativité des observations du fait du biais d’échantillonages et de la relative fiabilité des répondants. La disponibilité grandissantes de bases de données collectées passivements couplé à la forte
pénétration des smartphones ont ouvert des perspectives intéressantes concernant la collecte et le traitement automatisé de données de mobilité.----------ABSTRACT This thesis proposes a methodology to mine valuable nformation about the usage of a facility (e.g. building), based only on Wi-Fi network connection history. Data are collected at Concordia University in Montreal, Canada, during one week in Febuary 2015. Using the Wi-Fi access log data, we characterize activities taking place within a building without any additional knowledge of the building itself. Such information can be used to monitor the use of a facility automatically, to study human mobility or as an input information for mobility models
Biologically inspired methods for organizing distributed services on sensor networks
Tales HeimfarthPaderborn, Univ., Diss., 200
The Impact of Digital Technologies on Public Health in Developed and Developing Countries
This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic
Improving Access and Mental Health for Youth Through Virtual Models of Care
The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial