A wireless sensor network (WSN) is a self-organizing data gathering network, which consists of small sensor nodes that have several capabilities ？ sensing, computing and wireless communication. Small sensor nodes (also called smart sensor nodes) sense various events such as variations in temperature...
A wireless sensor network (WSN) is a self-organizing data gathering network, which consists of small sensor nodes that have several capabilities ？ sensing, computing and wireless communication. Small sensor nodes (also called smart sensor nodes) sense various events such as variations in temperature, illumination, sound. Sensor nodes can be deployed in an ad-hoc fashion, and operate via wireless communication. Because of these self-configuration abilities, WSN is attractive in many fields like the military, environmental research, industry and home.
However, to make sensor network technologies practical in the real-world, there remain several research issues which need to be resolved, such as minimizing sensor reading errors for reliability, efficient energy consumption for prolonging the network lifetime, and real-time data delivery for availability and utility.
At the aspect of sensor reading error, we can identify two types of problems with unreliable sensor readings: the user system and the sensor network itself. At the aspect of an application service, sensor reading errors cause service faults to users. For example, if temperature sensors report inaccurate data to a system which controls an air conditioner and a heater, the system may perform an inappropriate task, such as turning on the air conditioner in the winter. Also, at the aspect of a sensor network, sensor reading errors result in high traffic and processing overheads, which causes inefficient energy consumption and communication latency.
At the aspect of efficient energy consumption, un-rechargeable characteristics of sensor node, the place sensor node deployed and a great number of sensor nodes cause limited network lifetime of sensor network. There are two different kinds of problems in energy consumption. One is inefficient energy consumption, and the other is unbalanced energy consumption. Inefficient energy consumption is caused by unnecessary energy consumption like idle listening, overhearing and so on. It lowers overall network energy states and reduced network lifetime. Unbalanced energy consumption is caused by WSNs characteristics like inequality of event sensing and centralized data transmissions. It makes WSN lost its sensing spaces (hole problem) and paralyzes the network functions in spite of available resources that is enough to keep on network functions. From the node-level point of view, idle listening, overhearing, collision and control packet overhead are main reasons of inefficient energy consumption. And from the network-level point of view, unnecessary duplication of same data packet and always using optimal routing path are main reasons of energy inefficiency. In the case of unbalanced energy consumption, inequality of event sensing, disregard of remained energy and different distance from the sink node are main reasons.
At the respect of real-time data delivery, even though WSN has data reliability and enough lifetime, it is useless if it can？t deliver the data to end user in time. Delivery delay of sensing data such as flood, fire, earthquake is disaster
To make sensor network technologies practical in the real-world, these kinds of problems must be solved by providing reliability, energy efficiency and real-time delivery. For these, we studied data verification and data aggregation.
Unreliable data in WSN called faulty sensor reading. We classify faulty sensor readings into sensor faults and measurement errors, then propose a novel in-network data verification algorithm which includes adaptive fault checking, measurement error elimination and data refinement. The proposed algorithm eliminates faulty readings as well as refines normal sensor readings, to increase reliability
And, by data aggregation, we achieved energy efficiency and real-time delivery. Data aggregation is good choice in WSN because it enhances energy efficiency and minimizes communication delay by reducing data volume and redundancy.
A wireless sensor network (WSN) is a self-organizing data gathering network, which consists of small sensor nodes that have several capabilities ？ sensing, computing and wireless communication. Small sensor nodes (also called smart sensor nodes) sense various events such as variations in temperature, illumination, sound. Sensor nodes can be deployed in an ad-hoc fashion, and operate via wireless communication. Because of these self-configuration abilities, WSN is attractive in many fields like the military, environmental research, industry and home.
However, to make sensor network technologies practical in the real-world, there remain several research issues which need to be resolved, such as minimizing sensor reading errors for reliability, efficient energy consumption for prolonging the network lifetime, and real-time data delivery for availability and utility.
At the aspect of sensor reading error, we can identify two types of problems with unreliable sensor readings: the user system and the sensor network itself. At the aspect of an application service, sensor reading errors cause service faults to users. For example, if temperature sensors report inaccurate data to a system which controls an air conditioner and a heater, the system may perform an inappropriate task, such as turning on the air conditioner in the winter. Also, at the aspect of a sensor network, sensor reading errors result in high traffic and processing overheads, which causes inefficient energy consumption and communication latency.
At the aspect of efficient energy consumption, un-rechargeable characteristics of sensor node, the place sensor node deployed and a great number of sensor nodes cause limited network lifetime of sensor network. There are two different kinds of problems in energy consumption. One is inefficient energy consumption, and the other is unbalanced energy consumption. Inefficient energy consumption is caused by unnecessary energy consumption like idle listening, overhearing and so on. It lowers overall network energy states and reduced network lifetime. Unbalanced energy consumption is caused by WSNs characteristics like inequality of event sensing and centralized data transmissions. It makes WSN lost its sensing spaces (hole problem) and paralyzes the network functions in spite of available resources that is enough to keep on network functions. From the node-level point of view, idle listening, overhearing, collision and control packet overhead are main reasons of inefficient energy consumption. And from the network-level point of view, unnecessary duplication of same data packet and always using optimal routing path are main reasons of energy inefficiency. In the case of unbalanced energy consumption, inequality of event sensing, disregard of remained energy and different distance from the sink node are main reasons.
At the respect of real-time data delivery, even though WSN has data reliability and enough lifetime, it is useless if it can？t deliver the data to end user in time. Delivery delay of sensing data such as flood, fire, earthquake is disaster
To make sensor network technologies practical in the real-world, these kinds of problems must be solved by providing reliability, energy efficiency and real-time delivery. For these, we studied data verification and data aggregation.
Unreliable data in WSN called faulty sensor reading. We classify faulty sensor readings into sensor faults and measurement errors, then propose a novel in-network data verification algorithm which includes adaptive fault checking, measurement error elimination and data refinement. The proposed algorithm eliminates faulty readings as well as refines normal sensor readings, to increase reliability
And, by data aggregation, we achieved energy efficiency and real-time delivery. Data aggregation is good choice in WSN because it enhances energy efficiency and minimizes communication delay by reducing data volume and redundancy.
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