Many sensor applications often require collecting raw sensed values from many sensor nodes to one centralised server. sensor data collection typically comes with various quality requirements, e.g. the level of precisi...
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Many sensor applications often require collecting raw sensed values from many sensor nodes to one centralised server. sensor data collection typically comes with various quality requirements, e.g. the level of precision requested for temperature values, the time constraints for getting the data, or the percentage of data that is needed. This paper presents a quality-aware sensing framework where characterisations of sensor applications' quality needs are identified and different sensor data collection problems are classified. Two problems and their solutions are then presented as examples to demonstrate how single (or multiple) quality need(s) are satisfied. The paper concludes with suggestions for future research directions that have the potential to complete the framework and provide a holistic approach to sensor applications with diverse quality requirements.
Wireless sensor networks (WSNs) enable the collection of physical measurements over a large geographic area. It is often the case that the authors are interested in computing and tracking the spatial-average of a func...
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Wireless sensor networks (WSNs) enable the collection of physical measurements over a large geographic area. It is often the case that the authors are interested in computing and tracking the spatial-average of a function of the sensor measurements over a region covered by the WSN. Unfortunately, conventional methods need a large number of channel uses for collecting sensordata in the case of a large amount of sensors. They propose a novel computation scheme over fading multiple-access channels based on the asymptotic free behaviour of random matrices and the property of limit spectrum distribution of random matrices. The proposed scheme can greatly reduce the channel uses and does not need channel estimation. Moreover, the performance evaluations over multiple-input-multiple-output (MIMO) and MIMO-orthogonal frequency division multiplexing system demonstrate that the proposed method offers reliable mean computation of common functions of sensordata even for the case of small sample sizes.
In mobile sensor networks (MSNs), since sensor nodes and wireless networks are highly resource constrained and, it is highly required to manage sensordata in flexible and efficient manners. Under the MEXT research pr...
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ISBN:
(纸本)9781479926527
In mobile sensor networks (MSNs), since sensor nodes and wireless networks are highly resource constrained and, it is highly required to manage sensordata in flexible and efficient manners. Under the MEXT research project(1) entitled "Studies on Efficient data Processing Techniques for Mobile sensor Networks," we have conducted researches on data management issues in MSNs. In this paper, we report some of our achievements in a sub-area of this project, which addresses data transmission for efficient datacollection in MSNs. In particular, we first show our achievements on how to efficiently transmit sensordata from sensor nodes to mobile sink nodes considering the fairness and the amount of sensordata collected. Then, we also show our achievements on how to enlarge sensor data collection area and how to reduce communication traffic using mobile sink nodes.
The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or 'things' where things can sense, communicate, compute and potentially actuate as w...
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ISBN:
(纸本)9781479925049
The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or 'things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensordata to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensordata before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensordata processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile sensordata Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensordata without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform.
The nature of many sensor applications as well as continuously changing sensordata often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwi...
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The nature of many sensor applications as well as continuously changing sensordata often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely sensor data collection using Distributed Graph Coloring) - a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection. Published by Elsevier B.V.
This paper proposes an efficient message collection scheme for wireless mesh networks and measures its performance. Built upon the WirelessHART protocol providing a slot-based deterministic network access as well as t...
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ISBN:
(纸本)9783642135767
This paper proposes an efficient message collection scheme for wireless mesh networks and measures its performance. Built upon the WirelessHART protocol providing a slot-based deterministic network access as well as the split-merge operation capable of switching channels within a single slot, the proposed scheme allocates two consecutive slots to each pair of child nodes for a receiver. In addition to the primary sender, the secondary sender tries to send a message, if it has, after the predefined interval (additional transmit), while the receiver selects the frequency channel bound to each sender according to the clear channel assessment result in a single slot (additional receive). The simulation result, obtained from the discrete event scheduler targeting at the 4-level binary tree topology, shows that the proposed scheme can improve the message delivery ratio by up to 35.4% and reduce the wasteful transmission by up to 38 % for the given slot error ranges, compared with the non-switching scheme.
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