Surveillance operations include the timely detection, localization, recognition and identification of objects and events, their relationships, activities, and plans, in a given Volume Of Interest (VOI). distributed se...
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ISBN:
(纸本)9781424443970
Surveillance operations include the timely detection, localization, recognition and identification of objects and events, their relationships, activities, and plans, in a given Volume Of Interest (VOI). distributedsensors when properly managed can cooperatively provide a complete, accurate, and timely information about the presence and activity of all objects or events within a VOI. This paper presents a holonic sensor management approach that can be used to handle cooperative tasks such as target cueing and handoff in military surveillance operations.
In this paper, we consider a cognitive radio wireless sensor network (CR-WSN), where each sensor node is equipped with cognitive radio. A typical concern in CR-WSN is energy consumption due to resource-constrained nat...
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ISBN:
(纸本)9781479947195
In this paper, we consider a cognitive radio wireless sensor network (CR-WSN), where each sensor node is equipped with cognitive radio. A typical concern in CR-WSN is energy consumption due to resource-constrained nature of sensor nodes. Moreover, additional energy is consumed in a CR-WSN to support CR-exclusive functionality such as spectrum sensing and switching, which could shorten sensor node lifetime. However, some sensor nodes could receive similar signal due to similar channel condition such that they probably have same spectrum sensing results. Consequently, we propose a clustering based scheme for spectrum sensing in CR-WSN, which reduces energy consumption by involving less nodes in spectrum sensing. With our improved clustering algorithm, sensor nodes are grouped into different sets based on their similarity in sensing result. In order to identify the optimal cluster number, a new objective function, based on new intra-cluster and inter-cluster proximity measures has been proposed in our study. The simulation results show that the proposed scheme can effectively reduce the energy consumption of sensor node and improve global detection probability.
Efficient scheduling of time slots in a time division multiple access scheme (TDMA) is important for low power wireless sensor networks. Existing algorithms are either centralized with poor scalability, or distributed...
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ISBN:
(纸本)9781424413119
Efficient scheduling of time slots in a time division multiple access scheme (TDMA) is important for low power wireless sensor networks. Existing algorithms are either centralized with poor scalability, or distributed but with high complexity. In this paper, we explain how TDMA could be more energy efficient by careful slot scheduling in wireless sensor networks. Then we propose a deterministic distributed TDMA scheduling algorithm (DD-TDMA). In DD-TDMA, each sensor node schedules its own TDMA slot based on its neighborhood information, and packet collisions are gracefully avoided during scheduling. The experimental results show that compared to other centralized and distributed scheduling algorithms, DD-TDMA achieves better performance in terms of schedule length, running time and message complexity.
With the development of Internet of Things and multi-sensor data fusion technology, traditional off-line data processing methods have been unable to deal with massive data, which leads to a loss in timeliness of data....
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ISBN:
(纸本)9781509063529
With the development of Internet of Things and multi-sensor data fusion technology, traditional off-line data processing methods have been unable to deal with massive data, which leads to a loss in timeliness of data. As more and more well-known companies start to focus on real-time big data applications, some computing frameworks have developed rapidly, in which Apache Storm is the most representative open-source, distributed one due to its high reliability and good processing mode. In this paper, a multi-sensor data fusion system that combines classical data fusion algorithms with Storm architecture is proposed, to achieve real-time streaming data processing. Data fusion process is split into functional modules according to the feature of Storm. Extensive experiment results show that these algorithms can achieve better performance of data fusion in a short delay.
In this paper, we explore two important issues for distributed target detection in wireless sensor network (WSN): (1) How to increase efficient-energy for each sensor;(2) How to improve the detection precision about w...
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ISBN:
(纸本)9781424413119
In this paper, we explore two important issues for distributed target detection in wireless sensor network (WSN): (1) How to increase efficient-energy for each sensor;(2) How to improve the detection precision about whether a target is occurring in the event region. We present a neighbor collaboration detection (NCD) scheme that introduces a clustered fusion into target detection process. Based on MAP decision criterion, we propose an optimal fusion decision. We mathematically show that the NCD Scheme vastly improves the target detection precision, when the number of neighbor sensor is large.
The distributed estimator will need lower communication bandwidth and computational resources. Consequently, the distributed estimator using wireless sensor networks increases in the reliability, robustness and surviv...
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ISBN:
(纸本)9781538649855
The distributed estimator will need lower communication bandwidth and computational resources. Consequently, the distributed estimator using wireless sensor networks increases in the reliability, robustness and survivability of the system since subsystem do not need to transmit their unprocessed observations to the central station. In order to estimates the system states, we first expand nonlinear cyber physical systems such as power system model using Tailor series then it uses extended Kalman Filter (EKF) to estimate the local system states. The coordinator estimator is linearly combined with the information received from local estimators with learning rate parameter.
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