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Abstract-Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.

Solar vapor generation has presented great potential for wastewater treatment and seawater desalination with high energy conversion and utilization efficiency. However, technology gaps still exist for achieving a fast evaporation rate and high quality of water combined with low‐cost deployment to provide a sustainable solar‐driven water purification system. In this study, a naturally abundant biomass, konjac glucomannan, together with simple‐to‐fabricate iron‐based metal‐organic framework‐derived photothermal nanoparticles is introduced into the polyvinyl alcohol networks, building hybrid hydrogel evaporators in a cost‐effective fashion ($14.9 m−2 of total materials cost). With advantageous features of adequate water transport, effective water activation, and anti‐salt‐fouling function, the hybrid hydrogel evaporators achieve a high evaporation rate under one sun (1 kW m−2) at 3.2 kg m−2 h−1 out of wastewater with wide degrees of acidity and alkalinity (pH 2–14) and high‐salinity seawater (up to 330 g kg−1). More notably, heavy metal ions are removed effectively by forming hydrogen and chelating bonds with excess hydroxyl groups in the hydrogel. It is anticipated that this study offers new possibilities for a deployable, cost‐effective solar water purification system with assured water quality, especially for economically stressed communities.

Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage and application services to end users. In this article, we elaborate the motivation and advantages of Fog computing and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state of the art of Fog computing and similar work under the same umbrella. Distinguished from other reviewing work of Fog computing, this paper further discloses the security and privacy issues according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of system security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device. In addition, we discuss the authentication and authorization techniques that can be used in Fog computing. An example of authentication techniques is introduced to address the security scenario where the connection between Fog and Cloud is fragile.In the past few years, Cloud computing has provided many opportunities for enterprises by offering their customers a range of computing services. Current 'pay-as-you-go' Cloud computing model becomes an efficient alternative to owning and managing private data centres for customers facing Web applications and batch processing [8]. Cloud computing frees the enterprises and their end users from the specification of many details, such as storage resources, computation limitation and network communication cost. However, this bliss becomes a problem for latency-sensitive applications, which require nodes in the vicinity to meet their delay requirements [2]. When techniques and devices of IoT are getting more involved in people's life, current Cloud computing paradigm can hardly satisfy their requirements of mobility support, location awareness and low latency.Fog computing is proposed to address the aforementioned problem [1]. As Fog computing is implemented at the edge of the network, it provides low latency, location awareness and improves quality-of-services (QoS) for streaming and real time applications. Typical examples include industrial automation, transportation and networks of sensors and actuators. Moreover, this new infrastructure supports heterogeneity as Fog devices include end-user devices, access points, edge routers and switches. The Fog paradigm is well positioned for real time big data analytics, supports densely distributed data collection points and provides advantages in entertainment, advertising, personal computing and other applications. WHAT CAN WE DO WITH FOG?We elaborate on the role of Fog computing in the following six motivating scenarios. The advantages of Fog computing satisfy the requirements of applications in these scenarios.Smart Grid: Energy load balancing applications may run on network edge devices, such as smart metres and micro-grids [9]. Ba...

Solar‐driven interfacial evaporation provides a promising method for sustainable freshwater production. However, high energy consumption of vapor generation fundamentally restricts practicality of solar‐driven wastewater treatment. Here a facile strategy is reported to control the hydration of polymer network in hydrogels, where densely cross‐linked polymers serving as a framework are functionalized by a highly hydratable polymeric network. The hydration of polymer chains generates a large amount of weakly bounded water molecules, facilitating the water evaporation. As a result, the hydrogel‐based solar evaporator can extract water from a variety of contaminants such as salts, detergents, and heavy metal components using solar energy with long‐term durability and stability. This work demonstrates an effective way to tune the interaction between water and materials at a molecular level, as well as an energy‐efficient water treatment technology toward wastewater containing complex contaminants.

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, as is known, machine learning models lack robustness to adversarial examples, which are crafted by adding minor, yet carefully chosen, perturbations to the normal inputs. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features (e.g., requested permissions, specific API calls, etc.), and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective due to the rising challenge in adding perturbations to Dalvik bytecode without affecting their original functionality.In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model (i.e., a Deep Neural Network). Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well (e.g., Support Vector Machine in Drebin or Random Forest in MaMaDroid). We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware examples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.

Extracting ubiquitous atmospheric water is a sustainable strategy to enable decentralized access to safely managed water but remains challenging due to its limited daily water output at low relative humidity (≤30% RH). Here, we report super hygroscopic polymer films (SHPFs) composed of renewable biomasses and hygroscopic salt, exhibiting high water uptake of 0.64–0.96 g g−1 at 15–30% RH. Konjac glucomannan facilitates the highly porous structures with enlarged air-polymer interfaces for active moisture capture and water vapor transport. Thermoresponsive hydroxypropyl cellulose enables phase transition at a low temperature to assist the release of collected water via hydrophobic interactions. With rapid sorption-desorption kinetics, SHPFs operate 14–24 cycles per day in arid environments, equivalent to a water yield of 5.8–13.3 L kg−1. Synthesized via a simple casting method using sustainable raw materials, SHPFs highlight the potential for low-cost and scalable atmospheric water harvesting technology to mitigate the global water crisis.

This perspective article reviews recent progress in rational synthesis of conductive polymer hydrogels utilizing doping principles and their applications in advanced sensor technologies.