Low Power Wide Area Network (LPWAN) is designed for low-bandwidth, low-power, long-distance, large-scale connected IoT applications and realistic for networking in an emergency or restricted situation, so it has been proposed as an attractive communication technology to handle unexpected situations that occur during and/or after a disaster. However, the traditional LPWAN with its default protocol will reduce the communication efficiency in disaster situation because a large number of users will send and receive emergency information result in communication jams and soaring error rates. In this paper, we proposed a LPWAN based decentralized network structure as an extension of our previous Disaster Information Sharing System (DISS). Our network structure is powered by Named Node Networking (3N) which is based on the Information-Centric Networking (ICN). This network structure optimizes the excessive useless packet forwarding and path optimization problems with node name routing (NNR). To verify our proposal, we conduct a field experiment to evaluate the efficiency of packet path forwarding between 3N+LPWA structure and ICN+LPWA structure. Experimental results confirm that the load of the entire data transmission network is significantly reduced after NNR optimized the transmission path.

We propose the node name routing (NNR) strategy for information-centric ad-hoc networks based on the named-node networking (3N). This strategy is especially valuable for use in disaster areas because, when the Internet is out of service during a disaster, our strategy can be used to set up a self-organizing network via cell phones or other terminal devices that have a sharing ability, and it does not rely on a base station (BS) or similar providers. Our proposed strategy can solve the multiple-name problem that has arisen in prior 3N proposals, as well as the dead loop problems in both 3N ad-hoc networks and TCP/IP ad-hoc networks. To evaluate the NNR strategy, it is compared with the optimized link state routing protocol (OLSR) and the dynamic source routing (DSR) strategy. Computer-based comprehensive simulations showed that our NNR proposal exhibits a better performance in this environment when all of the users are moving randomly. We further observed that with a growing number of users, our NNR protocol performs better in terms of packet delivery, routing cost, etc.

Information Centric Networking (ICN) had merits in terms of mobility, security, power consumption and network traffic. When a large-scale disaster occurred, the current communication system might be fragile and the server based network service might be unavailable due to the damages, network congestions, and power failure, etc. In this paper, we proposed an ICN based Disaster Information Sharing Service (DISS) [1], [2] system. DISS could provide robust information sharing service. Users could publish disaster information as a content message with the help of our DISS. In addition, by utilizing DISS's message naming strategy, users could retrieve disaster information even without a server connection. The ICN based DISS could reduce the probability of network congestion when a large number of simultaneous connections occurring. It could provide server-less service in poor network condition. DISS allows users retrieve disaster information from terminals or ICN nodes. During disasters, sharing information timely and effective could protect people from disaster, ensure people's safety.

The rise of next-generation logistics systems featuring autonomous vehicles and drones has brought to light the severe problem of Global navigation satellite system (GNSS) location data spoofing. While signal-based anti-spoofing techniques have been studied, they can be challenging to apply to current commercial GNSS modules in many cases. In this study, we explore using multiple sensing devices and machine learning techniques such as decision tree classifiers and Long short-term memory (LSTM) networks for detecting GNSS location data spoofing. We acquire sensing data from six trajectories and generate spoofing data based on the Software-defined radio (SDR) behavior for evaluation. We define multiple features using GNSS, beacons, and Inertial measurement unit (IMU) data and develop models to detect spoofing. Our experimental results indicate that LSTM networks using ten-sequential past data exhibit higher performance, with the accuracy F1 scores above 0.92 using appropriate features including beacons and generalization ability for untrained test data. Additionally, our results suggest that distance from beacons is a valuable metric for detecting GNSS spoofing and demonstrate the potential for beacon installation along future drone highways.

Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM.