In N-policy, the nodes attempt to seize the channel when the number of packets in the buffer approaches N. The performance of N-policy on the energy efficiency is widely studied in the past years. And it is presented ...In N-policy, the nodes attempt to seize the channel when the number of packets in the buffer approaches N. The performance of N-policy on the energy efficiency is widely studied in the past years. And it is presented that there exists one optimal N to minimize the energy consumption. However, it is noticed that the delay raised by N-policy receives little attention. This work mathematically proves the delay to monotonically increase with increasing N in the collision-unfree channel. For planar network where the near-to-sink nodes burden heavier traffic than the external ones, the data stemming from the latter undergo longer delay.The various-N algorithm is proposed to address this phenomenon by decreasing the threshold N of outer nodes. Without the impacting on the network longevity, the maximum delay among the network has decreased 62.9% by the algorithm. Extensive simulations are given to verify the effectiveness and correctness of our analysis.展开更多
In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network conn...In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network connectivity during activity(working or sleeping) scheduling,enabling more sensors to be switched to sleeping mode and thus more energy savings.In this paper,the nodal behavior in such delay-tolerant WSNs(DT-WSNs) is modeled and analyzed.The maximum hop count with a routing path is derived in order not to violate a given sensor-to-sink delay constraint,along with extensive simulation results.展开更多
In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e...In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.展开更多
IEEE802.15.4 is the communication protocol meeting the requirements of short distance and low transmission rate, and achieving the objective of low power. The technique is an emerging wireless communication standard. ...IEEE802.15.4 is the communication protocol meeting the requirements of short distance and low transmission rate, and achieving the objective of low power. The technique is an emerging wireless communication standard. It is a two-way wireless communication technology with short distance, low complexity, low power consumption and low data transmission rate and low cost. The paper analyzes the features, framework, performance indicators and overall hardware platform structure of JN5121 chip which is used in JENNIC Corporation. And the paper researches the characteristics of actual oroducts for project development.展开更多
基金Projects(61379110,61379057,61073186)supported by the National Natural Science Foundation of ChinaProject(2013zzts043)supported by the Fundamental Research Funds for the Central Universities,China
文摘In N-policy, the nodes attempt to seize the channel when the number of packets in the buffer approaches N. The performance of N-policy on the energy efficiency is widely studied in the past years. And it is presented that there exists one optimal N to minimize the energy consumption. However, it is noticed that the delay raised by N-policy receives little attention. This work mathematically proves the delay to monotonically increase with increasing N in the collision-unfree channel. For planar network where the near-to-sink nodes burden heavier traffic than the external ones, the data stemming from the latter undergo longer delay.The various-N algorithm is proposed to address this phenomenon by decreasing the threshold N of outer nodes. Without the impacting on the network longevity, the maximum delay among the network has decreased 62.9% by the algorithm. Extensive simulations are given to verify the effectiveness and correctness of our analysis.
基金Sponsored by the Shanghai Education Bureau(Grant No. 11YZ93,A-3101-10-035)the Shanghai Baiyulan Funding(Grant No. 2010B086)the National Natural Science Foundation of China(Grant No. 61003215)
文摘In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network connectivity during activity(working or sleeping) scheduling,enabling more sensors to be switched to sleeping mode and thus more energy savings.In this paper,the nodal behavior in such delay-tolerant WSNs(DT-WSNs) is modeled and analyzed.The maximum hop count with a routing path is derived in order not to violate a given sensor-to-sink delay constraint,along with extensive simulation results.
文摘In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.
文摘IEEE802.15.4 is the communication protocol meeting the requirements of short distance and low transmission rate, and achieving the objective of low power. The technique is an emerging wireless communication standard. It is a two-way wireless communication technology with short distance, low complexity, low power consumption and low data transmission rate and low cost. The paper analyzes the features, framework, performance indicators and overall hardware platform structure of JN5121 chip which is used in JENNIC Corporation. And the paper researches the characteristics of actual oroducts for project development.
