The increase of network size and sensory data leads to many serious problems to the wireless sensor networks due to the limited energy. Data prediction method is helpful to reduce network traffic and increase the network lifetime accordingly, especially by exploring data correlation among the sensory data. Data prediction can also be used to recover abnormal/lost data in case these sensor nodes fail to work. The current prediction methods in wireless sensor networks do not make full usage of the spatial-temporal correlation between wireless sensor nodes, and thus leads to higher prediction error relatively. This paper proposes a novel model for multi-step sensory data prediction in wireless sensor network. Firstly, we introduce the artificial neural networks based on 1-D CNN (One-Dimensional Convolutional Neural Network) and Bi-LSTM (Bidirectional Long and Short-Term Memory) to get the abstract features of different attributes via the pre-processed sensory data. Then, these abstract features are used to obtain one-step prediction. Finally, the multi-step prediction is introduced by using historical data and the prediction results of the previous step iteratively. Experiment results show that after selecting suitable node combinations in which the spatial-temporal correlation is highlighted, the proposed multi-step predictive model can predict multi-step (short and medium term) sensory data, and its performance is better compared with other related methods.

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Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM

With the rapid development of software-defined technologies, emerging multimedia applications are booming, which require real-time communication and computation via devices. Meanwhile, the program codes in multimedia applications need to be updated periodically to accommodate changes in the edge networking environment. Therefore, a huge number of smart sensing devices which are deployed in the infrastructures of smart city can play a bigger role than ever before due to their program codes in multimedia applications can be updated by sensing data. However, how to spread program codes to a large amount of devices which are distributed in smart city in a low-cost and fast way is a challenging issue. To solve the issue, in this paper, an Unmanned Aerial Vehicles (UAVs) joint Vehicles as Data Mules for Fast Codes Dissemination (UVDCD) scheme is proposed to spread codes as a fast and low-cost pattern for edge networking in smart city. In UVDCD scheme, first of all, large amount of vehicles in smart city act as mules for code dissemination. Although the cost of the method is low and can quickly cover most networks, there is a very long trailing phenomenon for this approach, i.e. in the early stage of code dissemination, the code dissemination rate increases rapidly over time and have a high efficiency, but after a short time, the increase of code dissemination rate has become very slowly over time. So, in this situation, the unmanned aerial vehicle is used to spread the program codes of intelligent devices where vehicles are hard to spread, thereby eliminating the trailing phenomenon in code dissemination. In order to achieve a high dissemination efficiency for UAV, first, we cluster the device nodes which have not received codes, then we select the optimized UAV flight trajectory based on the cluster so that the total length of the UAV flight path is the shortest, i.e. low-cost, and the number of devices which can receive codes is the largest. Finally, the validity of UVDCD scheme is confirmed based on real vehicle data, and through experiments, it can effectively overcome the trailing phenomenon in code dissemination after using UAV. The coverage ratio and dissemination speed are higher than those of previous strategies and increase 2.11% and 69.44% respectively.

UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City

Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6–57.9% and reduce the delay by 0.7–17.9%.

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A low redundancy data collection scheme to maximize lifetime using matrix completion technique

The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.

A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

A blockchain-empowered AAA scheme in the large-scale HetNet

Natural disaster is one of the most important research topics worldwide. In this paper, a data driven cyber-physical system is introduced to detect landslides. This system is composed of Wi-Sun acceleration sensors, which can detect the acceleration of the nearby environment in 3D domain, and the sensors are linked with the router (act as 'sink' node) via Wi-Sun transmission (i.e. IEEE802.15.4g). The details of the detection system are explained and the landslide detection mechanism with low computational complexity is proposed. A traffic reduction method is proposed thereafter to help reduce the data needed for transmission by exploring the intra-correlations of the sensor data. This method can save the energy consumption without degrading the detection performance. Field test is conducted and the results show that the landslide can be detected and amount of data to be transmitted can be reduced, which verifies the system's effectiveness.

Data Driven Cyber-Physical System for Landslide Detection

A linear-cell-based radio-over-fiber (LC-RoF) system is proposed and demonstrated for efficient mobile communication in high-speed trains without hard handover processes. The configuration of the LC-RoF network and possible router architectures are discussed for the field trial test of the proposed system and for advanced systems in the future. Via the LC-RoF network, 90-GHz, millimeter-wave radio access between the ground and a train car is operated in a centralized manner. The resulting throughput of 1.5 Gbit/s is achieved in the field trial test, which is comprised of 250-Mbaud four-subcarrier differential quadrature phase-shift keying with a forward-error correction code implemented in field-programmable gate arrays to the Shinkansen train traveling at 240 km/h without any interruptions in the connections at the change of radio access units.

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High-Speed Railway Communication System Using Linear-Cell-Based Radio-Over-Fiber Network and Its Field Trial in 90-GHz Bands

We demonstrate 97-GHz millimeter-wave radio access system connected to bidirectional radio over fiber network for high-speed train systems. Obtained throughput from a central office to a 240-km/h train car is achieved 1.5 Gbps.

Field Trial of 1.5-Gbps 97-GHz Train Communication System Based on Linear Cell Radio Over Fiber Network for 240-km/h High-Speed Train

Recently, the use of millimeter waves in the railway environment has attracted attention In this paper, the application of the 90GHz band millimeter-wave to the radar system for detecting obstacles and to the ground-to-communication system for broad band communication in the railway is shown Then the result of the field tests using each prototype system is described and to possibility of realizing the applications is also described

Nagateru Iwasawa

Papers

Data Driven Cyber-Physical System for Landslide Detection

High-Speed Railway Communication System Using Linear-Cell-Based Radio-Over-Fiber Network and Its Field Trial in 90-GHz Bands

Field Trial of 1.5-Gbps 97-GHz Train Communication System Based on Linear Cell Radio Over Fiber Network for 240-km/h High-Speed Train

Study of the new application using the millimeter-wave in the railway

3F25 The Monitoring System Using 90GHz Band for Railway(Condition Monitoring-Infrastructure)