Development in the technology of sensor such as Micro Electro Mechanical Systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications have contributed a large transformation in Wireless Sensor Network (WSN) recently. It assists and improves work performance both in the field of industry and our daily life. Wireless Sensor Network has been widely used in many areas especially for surveillance and monitoring in agriculture and habitat monitoring. Environment monitoring has become an important field of control and protection, providing real-time system and control communication with the physical world. An intelligent and smart Wireless Sensor Network system can gather and process a large amount of data from the beginning of the monitoring and manage air quality, the conditions of traffic, to weather situations. In this paper, we discuss and review wireless sensor network applications for environmental monitoring. In order to implement a good monitoring system, there are several requirements to be followed. From the studies, it has been proved to be an alternative way to replace the conventional method that uses men force to monitor the environment. It is also proven that these approaches can improve the system performance, provide a convenient and efficient method and can also fulfill functional requirements.

Wireless Sensor Network Applications: A Study in Environment Monitoring System

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly ...

/pdf/a-review-of-machine-learning-applications-in-wildfire-3yit0f75yl.pdf

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.

Global navigation satellite systems (GNSS) like GPS but also Galileo, GLONASS, and Beidou represent an important infrastructure to our society. They provide position and timing for numerous applications. A GNSS is a rather complex system consisting of around 30 satellites, a number of monitor stations, plus a control center. Billions of GNSS receivers represent the user segment. The receivers and the monitor stations receive a weak satellite radio signal and thus are susceptible to interference like jamming or spoofing. This paper outlines the core operation principles of satellite navigation to describe those kind of interference and to analyze the impacts. Different types of interference attacks result in different effects at the target receiver. The attack schemes are categorized within this paper. Reported incidents are summarized as well as the potential impact of GNSS vulnerability on critical infrastructure. Well known countermeasures at user receiver level or at system level are outlined and discussed for their suitability. The political and socioeconomic context of GNSS vulnerability is described emphasizing the large impact of a potential disruption of the GNSS service.

Known Vulnerabilities of Global Navigation Satellite Systems, Status, and Potential Mitigation Techniques

Wireless sensor Networks (WSNs) deployed in distributed Internet of Things (IoT) applications should be integrated into the Internet According to the distributed architecture, sensor nodes measure data, process, exchange information, and perform collaboratively with other sensor nodes and end-users, which can be internal or external to the network In order to maintain the trustworthy connectivity and the accessibility of distributed IoT, it is important to establish secure links for end-to-end communication with a strong pervasive authentication mechanism However, due to the resource constraints and heterogeneous characteristics of the devices, traditional authentication and key management schemes are not effective for such applications This paper proposes a pervasive lightweight authentication and keying mechanism for WSNs in distributed IoT applications, in which the sensor nodes can establish secured links with peer sensor nodes and end-users The established authentication scheme PAuthKey is based on implicit certificates and it provides application level end-to-end security A comprehensive description for the scenario based behavior of the protocol is presented With the performance evaluation and the security analysis, it is justified that the proposed scheme is viable to deploy in the resource constrained WSNs

https://journals.sagepub.com/doi/pdf/10.1155/2014/357430

PAuthKey: A Pervasive Authentication Protocol and Key Establishment Scheme for Wireless Sensor Networks in Distributed IoT Applications

Public Key Cryptography (PKC) is widely used for broadcast authentication. Intensive use of PKC for broadcast authentication, however, is thought to be expensive to resource constrained sensor nodes. In this paper, we propose a novel PKC based broadcast authentication scheme using signature amortization for Wireless Sensor Networks (WSNs). The proposed scheme exploits only one Elliptic Curve Digital Signature Algorithm (ECDSA) signature to authenticate all broadcast messages. Thus, the overhead for the signature is amortized over all broadcast messages. Besides low overhead, the proposed scheme retains high security that is as strong as conventional PKC based broadcast authentication schemes. Moreover, the proposed scheme can achieve immediate authentication and does not require time synchronization. For the implementation of the proposed scheme, an efficient public key distribution protocol is also presented in this paper. Experimental results of a testbed show that the overhead for authenticating a broadcast message is reduced significantly.

PKC Based Broadcast Authentication using Signature Amortization for WSNs

A forest fire has long been a severe threat to the forest resources and human life. The threat could effectively be mitigated by timely and accurate detection. In this paper, we propose a novel accurate forest fire detection system using Wireless Sensor Networks (WSNs). In the proposed system, the detection accuracy is increased by applying the multi-criteria detection that an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network which fuses sensing data corresponding to multiple attributes of a forest fire into an alarm decision. Due to the utilization of the artificial neural network, the proposed system enjoys low overhead and the self-learning capability. Furthermore, we have developed a prototype consisting TelosB sensor nodes and carried out extensive experiments to study the performance of the proposed system. We have also developed a solar battery in order to persistently power the unattended sensor node deployed in the forest.

A Novel Accurate Forest Fire Detection System Using Wireless Sensor Networks

Time synchronization is crucial to Wireless Sensor Networks (WSNs) due to the requirement of coordination between sensor nodes. Existing secure time synchronization protocols of WSNs introduce high overhead when used for global time synchronization. In this paper, we propose a lightweight secure global time synchronization protocol for WSNs. In the proposed protocol, a broadcast synchronization packet makes all sensor nodes in the network synchronize with the trusted source. The synchronization packet is protected by a proposed broadcast authentication algorithm, which introduces asymmetry by transmitting hash values of secret keys in advance. It achieves immediate authentication and does not require the loose time synchronization. To defend the pulse-delay attacks, the arrival time of the synchronization packet is checked according to the estimated arrival time interval. The upper bound on the skew of the proposed protocol is proved. The message complexity in one period of the proposed protocol is O(n) where n represents the number of sensor nodes. The simulation results show that the maximum skew is within tens of milliseconds.

Lightweight secure global time synchronization for Wireless Sensor Networks

Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

In the resource constrained Wireless Sensor Networks (WSNs), the broadcast authentication is widely fulfilled by the emerging delayed authentication technique. The one level backward one-way key chain in the delayed authentication technique limits heavily the duration of the broadcast authentication. In this paper, we propose a long duration broadcast authentication scheme for WSNs. The proposed scheme uses a novel hierarchical key chain to extend the duration of broadcast authentication to be as long as the whole lifetime of WSNs. Moreover, the overhead of the proposed scheme is significantly reduced compared to that with one level key chain. Extensive experimental results show that the novel hierarchical key chain fully outperforms the one level key chain in terms of memory and computation.

Yongsheng Liu

Papers

PKC Based Broadcast Authentication using Signature Amortization for WSNs

A Novel Accurate Forest Fire Detection System Using Wireless Sensor Networks

Lightweight secure global time synchronization for Wireless Sensor Networks

Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

Long Duration Broadcast Authentication for Wireless Sensor Networks