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Time series prediction techniques reduce the number of messages generated at the application level, saving energy spent in the communication and, consequently, extending the network lifetime. Trickle is a well-known time series prediction mechanism commonly used to decrease the number of transmitted messages in Wireless Sensor Networks (WSN) and thus save energy. This paper presents the Space-Time...
In wireless sensor networks, sensor nodes are often deployed intensively within the sensing area in order to achieve effective monitoring, resulting in a high degree of correlation between them. There is a certain variation rule between node acquisition data and time. The current time correlation will result in an abundance of redundant data within the sensing area, so eliminating data redundancy...
This paper proposes an efficient decoupling model for information producer (IPD) (i.e., physical sensor) and information provider (IPV) toward a semantic sensor-cloud integration to improve Wireless Sensor Networks' (WSN) lifetime. In particular, while IPDs produce sensing information, their IPVs, which are designed as virtual sensors on sensor-cloud based on network function virtualization, are responsible...
Energy consumption in general is one of biggest challenges when it comes to wireless sensor networks (WSNs). Since the biggest amount of energy is used for communication, the most logical way to reduce the energy consumption is to reduce the number of packets transmitted between sensor and sink node. To address this issue, data reduction methods, which are predicting the measured values both at source...
The derivative based prediction (DBP) is an algorithm for reducing the number of messages needed to transfer the data samples from a wireless sensor node to a sink, in real-time. The algorithm computes a linear fit over the time series and sends only the updates of the linear model to the sink, when needed. This paper presents two extensions of the original algorithm that further decrease the number...
Missing data is an inevitable problem in wireless sensor network and the way missing values are handled can significantly affect the analysis results involving such data. To address data missing issues, spatial correlation and temporal correlation modeling can be applied. This paper aims at reviewing some popular spatial and temporal correlation based methods. The proposed review includes a critical...
In this paper, we present a method for regenerate and predict the values of sensors in a wireless sensor network without direct communication between sensor node and sink node based on least square approximation. The main disadvantage of Wireless Sensor Networks (WSNs) is their limited power source. The main power consumer in a sensor node is its radio transceiver. One of the most effective methods...
The autonomic management of large-scale distributed systems now allows performance improvement, availability, and security, while simultaneously reducing the effort and skills required of system administrators. One way that systems can support these abilities is by relying on a continuous monitoring service to keep track of the states of the targeted systems. However, it is challenging to achieve...
In this paper, occupancy pattern extraction and prediction in an intelligent inhabited environment is addressed. The results of this research will help elderly people to live independently in their own home longer and help them in case of an emergency. Using a wireless sensor network system, daily behavioral patterns of the occupant are extracted. This information is then used to build a behavioral...
Wireless sensor networks have received considerable attention in recent years and played an important role in data collection applications. Sensor nodes usually have limited supply of energy. Therefore, a major consideration for developing sensor network applications is to conserve the energy for sensor nodes. In this paper, we propose a novel energy-efficient data acquisition algorithm based on the...
Data aggregation is a current hot research area in sensor networks. Aiming at the time series data in sensor networks, we present GMSVM (Grey Model Support Vector Machines), a novel prediction model data aggregation of sensor networks. In this model, grey model (GM) prediction theory is introduced into support vector machines (SVM). And the RBF kernel function is improved by Riemannian geometry analysis...
Energy efficient data collection protocols are required in order to better manage the limited energy, memory and processing capabilities of sensor networks. In applications where data are collected in real time, efficient management of sensor radio assumes critical significance because communication is energy intensive. Moreover, specialised sensors exist which consume even more energy than radio...
Event detection is a critical task in sensor networks, especially for environmental monitoring applications. Traditional solutions to event detection are based on analyzing one-shot data points, which might incur a high false alarm rate because sensor data is inherently unreliable and noisy. To address this issue, we propose a novel Distributed Single-pass Incremental Clustering (DSIC) technique to...
In this paper, a novel strategy for data transmission that is based on prediction revision dynamic adjustment data gathering algorithm (PRDA) is proposed in WSNs. The key idea of the PRDA is to separate the data prediction and model computing, and the autoregressive process model is employed for prediction revision algorithm. The model computing of PRDA is conducted by sink node firstly according...
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