There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Power Aware Routing in Mobile Ad Hoc Networks

Fundamental statistics in psychology and education.

In this article we provide a comprehensive review of the existing literature on techniques and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a cross-layer design approach, which is likely to be needed for optimal performance. Throughout the article we identify and discuss open issues, and propose directions for future research in the area

/pdf/in-network-aggregation-techniques-for-wireless-sensor-3worsvl5sb.pdf

In-network aggregation techniques for wireless sensor networks: a survey

Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.

https://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Fall%202012/Literature/InformationFusionInSensorNetworks.pdf

Information fusion for wireless sensor networks: Methods, models, and classifications

Adaptive data aggregation scheme in clustered wireless sensor networks

In the fifth-generation of mobile communications, network slicing is used to provide an optimal network for various services as a slice. In this paper, we propose a radio access network (RAN) slicing method that flexibly allocates RAN resources using deep reinforcement learning (DRL). In RANs, the number of slices controlled by a base station fluctuates in terms of user ingress and egress from the base station coverage area and service switching on the respective sets of user equipment. Therefore, when resource allocation depends on the number of slices, resources cannot be allocated when the number of slices changes. We consider a method that makes optimal-resource allocation independent of the number of slices. Resource allocation is optimized using DRL, which learns the best action for a state through trial and error. To achieve independence from the number of slices, we show a design for a model that manages resources on a one-slice-by-one-agent basis using Ape-X, which is a DRL method. In Ape-X, because agents can be employed in parallel, models that learn various environments can be generated through trial and error of multiple environments. In addition, we design a model that satisfies the slicing requirements without over-allocating resources. Based on this design, it is possible to optimally allocate resources independently of the number of slices by changing the number of agents. In the evaluation, we test multiple scenarios and show that the mean satisfaction of the slice requirements is approximately 97%.

Flexible Resource Block Allocation to Multiple Slices for Radio Access Network Slicing Using Deep Reinforcement Learning

Agricultural systems using advanced information and communication (ICT) technology can produce high-quality crops in a stable environment while decreasing the need for manual labor. The system collects a wide variety of environmental data and provides the precise cultivation control needed to produce high value-added crops; however, there are the problems of packet transmission errors in wireless sensor networks or system failure due to having the equipment in a hot and humid environment. In this paper, we propose a reliable wireless control system for hydroponic tomato cultivation using the 400 MHz wireless band and the IEEE 802.15.6 standard. The 400 MHz band, which is lower than the 2.4 GHz band, has good obstacle diffraction, and zero-data-loss communication is realized using the guaranteed time-slot method supported by the IEEE 802.15.6 standard. In addition, this system has fault tolerance and a self-healing function to recover from faults such as packet transmission failures due to deterioration of the wireless communication quality. In our basic experiments, the 400 MHz band wireless communication was not affected by the plants’ growth, and the packet error rate was less than that of the 2.4 GHz band. In summary, we achieved a real-time hydroponic liquid supply control with no data loss by applying a 400 MHz band WSN to hydroponic tomato cultivation.

/pdf/a-reliable-wireless-control-system-for-tomato-hydroponics-3syjuxo9no.pdf

A Reliable Wireless Control System for Tomato Hydroponics

Energy efficiency has been known as the most significant problem in all facets of the wireless sensor network operations. In this paper, we propose a meta-data-based data aggregation scheme in the clustering wireless sensor networks. For all sensor nodes in the sensing range of an event can detect the event and generate the sensing data, the cluster head will receive the sensing data packets about the same event, and there is likely to be some redundancy. In our scheme, only one of the sensor nodes within the sensing range of an event in a cluster is selected to transmit the sensing data to the cluster head via the meta data negotiation. We also evaluate this scheme with the LEACH protocol. The simulation results show that the cluster protocol with our data aggregation scheme is effective in prolonging the network lifetime and supporting scalable data aggregation.

A Meta-Data-Based Data Aggregation Scheme in Clustering Wireless Sensor Networks

Information communication technology (ICT) is required in the field of agriculture to solve problems arising because of the aging of farmers and shortage of heirs. In particular, environmental sensors and cameras are widely used in existing agricultural support systems for easy data collection. Although the traditional purpose of these systems is naive monitoring and controlling of the environment, the propagation of advanced cultivation is now expected by applying the data to machine learning and data mining technologies. Therefore, we propose a novel multi-modal sliding window-based support vector regression (multi-modal SW-SVR) method for accurate prediction of complicated water stress, which is a plant status, from two data types, namely environmental and plant image data. The proposed method includes two methodologies, SW-SVR and deep neural network (DNN) as a multi-modal feature extractor for SW-SVR. SW-SVR, which we proposed previously, is a suitable learning method for data with time-dependent characteristics, such as plant status. Moreover, we propose a new image feature, remarkable moving objects detected by adjacent optical flow (ROAF), to enable DNN to extract essential features easily for predicting water stress. Compared with existing regression models and features, the proposed multi-modal SW-SVR with ROAF demonstrates more precise and stable water stress prediction.

Hiroshi Mineno

Papers

Adaptive data aggregation scheme in clustered wireless sensor networks

Flexible Resource Block Allocation to Multiple Slices for Radio Access Network Slicing Using Deep Reinforcement Learning

A Reliable Wireless Control System for Tomato Hydroponics

A Meta-Data-Based Data Aggregation Scheme in Clustering Wireless Sensor Networks

Multi-modal sliding window-based support vector regression for predicting plant water stress