Abstract-Traditionally, environmental monitoring is achieved by a small number of expensive and high precision sensing unities. Collected data are retrieved directly from the equipment at the end of the experiment and after the unit is recovered. The implementation of a wireless sensor network provides an alternative solution by deploying a larger number of disposable sensor nodes. Nodes are equipped with sensors with less precision, however, the network as a whole provides better spatial resolution of the area and the users can have access to the data immediately. This paper surveys a comprehensive review of the available solutions to support wireless sensor network environmental monitoring applications.

SUMMARYIt is foreseeable that any object in the near future will have an Internet connection-this is the Internet of Things vision. All these objects will be able to exchange and process information, most of them characterized by small size, power constrained, small computing and storage resources. In fact, connecting embedded low-power devices to the Internet is considered the biggest challenge and opportunity for the Internet. There is a strong trend of convergence towards an Internet-based solution and the 6LoWPAN may be the convergence solution to achieve the Internet of Things vision. Wireless mesh networks have attracted the interest of the scientific community in recent years. One of the key characteristics of wireless mesh networks is the ability to self-organize and self-configure. Mesh networking and mobility support are considered crucial to the Internet of Things success. This paper surveys the available solutions proposed to support routing and mobility over 6LoWPAN mesh networks.

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Low power over wireless personal area networks (LoWPAN), in particular wireless sensor networks, represent an emerging technology with high potential to be employed in critical situations like security surveillance, battlefields, smart-grids, and in e-health applications. The support of security services in LoWPAN is considered a challenge. First, this type of networks is usually deployed in unattended environments, making them vulnerable to security attacks. Second, the constraints inherent to LoWPAN, such as scarce resources and limited battery capacity, impose a careful planning on how and where the security services should be deployed. Besides protecting the network from some well-known threats, it is important that security mechanisms be able to withstand attacks that have not been identified before. One way of reaching this goal is to control, at the network access level, which nodes can be attached to the network and to enforce their security compliance. This paper presents a network access security framework that can be used to control the nodes that have access to the network, based on administrative approval, and to enforce security compliance to the authorized nodes.

Network security encloses a wide set of technologies dealing with intrusions detection. Despite the massive adoption of signature-based network intrusion detection systems (IDSs), they fail in detecting zero-day attacks and previously unseen vulnerabilities exploits. Behaviour-based network IDSs have been seen as a way to overcome signature-based IDS flaws, namely through the implementation of machine-learning-based methods, to tolerate new forms of normal network behaviour, and to identify yet unknown malicious activities. A wide set of machine learning methods has been applied to implement behaviour-based IDSs with promising results on detecting new forms of intrusions and attacks. Innovative machine learning techniques have emerged, namely deep-learning-based techniques, to process unstructured data, speed up the classification process, and improve the overall performance obtained by behaviour-based network intrusion detection systems. The use of realistic datasets of normal and malicious networking activities is crucial to benchmark machine learning models, as they should represent real-world networking scenarios and be based on realistic computers network activity. This paper aims to evaluate CSE-CIC-IDS2018 dataset and benchmark a set of deep-learning-based methods, namely convolutional neural networks (CNN) and long short-term memory (LSTM). Autoencoder and principal component analysis (PCA) methods were also applied to evaluate features reduction in the original dataset and its implications in the overall detection performance. The results revealed the appropriateness of using the CSE-CIC-IDS2018 dataset to benchmark supervised deep learning models. It was also possible to evaluate the robustness of using CNN and LSTM methods to detect unseen normal activity and variations of previously trained attacks. The results reveal that feature reduction methods decreased the processing time without loss of accuracy in the overall detection performance.

Denial of service (DoS) attacks can be defined as any third-party action aiming to reduce or eliminate a network's capability to perform its expected functions. Although there are several standard techniques in traditional computing that mitigate the impact of some of the most common DoS attacks, this still remains a very important open problem to the network security community. DoS attacks are even more troublesome in smart object networks because of two main reasons. First, these devices cannot support the computational overhead required to implement many of the typical counterattack strategies. Second, low traffic rates are enough to drain sensors' battery energy making the network inoperable in short times. To realize the Internet of Things vision, it is necessary to integrate the smart objects into the Internet. This integration is considered an exceptional opportunity for Internet growth but, also, a security threat, because more attacks, including DoS, can be conducted. For these reasons, the prevention of DoS attacks is considered a hot topic in the wireless sensor networks scientific community. In this paper, an approach based on 6LowPAN neighbor discovery protocol is proposed to mitigate DoS attacks initiated from the Internet, without adding additional overhead on the 6LoWPAN sensor devices.Wireless sensor networks are a subtype of smart object networks, where the devices can interact with their environment by sensing and controlling physical parameters, such as temperature, humidity, and solar radiation. A single network may comprise hundreds of smart devices working together to accomplish a common task. Self-organization, fault-tolerance, and self-optimization are the main characteristics of smart object networks [2]. Currently, there are already many technologies that can be used to connect smart objects [3], most of them based on the standard IEEE 802.15.4 layer two protocol [6] but some being proprietary, such as ZigBee [7] and WirelessHART [8]. Nevertheless, these solutions are not compatible with IP protocol and consequently require complex gateways to connect them to the Internet. The aim is that, in a near future, users can access the information collected by smart objects from the Internet, using regular devices and standard protocols. To reach this aim, a new paradigm is necessary to enable smart objects to be accessed from the Internet where all devices and networks are IP-enabled, independently of their physical and media access control (MAC) layer protocol [1]. The support of Internet protocol (IP) in all smart devices will also simplify the application development because tools in use on regular computing for commissioning, configuring, managing, and debugging can be used or adapted. Initially, the IP protocol stack was considered too heavy to run on small power and resource constrained devices. Meanwhile, the scientific community, together with the industry, started to rethink many misconceptions about the use of IP in all devices and now the IPv6 protocol is considered the most consens...

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