The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.
Tremendous opportunities exist for enhancing water quality and improving aquatic habitat by actively managing urban water infrastructure to operate in conjunction with natural systems. The hyporheic zone (HZ) of streams, which is the area of active mixing between surface water and groundwater, is one such system that is overlooked by many water professionals, because the state of the science on this topic has not been transferred into practice. As a biogeochemically active zone, the HZ offers great potential to provide natural treatment of organic compounds, nutrients, and pathogens in urban streams, which are often strongly impacted by flow modifications and water pollution. Reliable treatment is most likely in streams in which the majority of flow occurs through the HZ, the flow is aerated, and sufficient residence times occur, which may be limited to specific channel morphologies and seasons. Integration of the HZ into stream management plans could also provide quality habitat in a landscape with increasingly depauperate biodiversity. Here, we review current knowledge on hydrological, chemical, and biological aspects of the HZ, with a focus on urban settings, and include a set of examples drawn from the literature of low-flow, effluent-dominated streams in which there is significant hyporheic flow and potential for contaminant attenuation. The HZ can be incorporated much more effectively into urban water management, including stream restoration efforts, by understanding the surface and subsurface features conducive to HZ flow and the water-quality and biodiversity improvements that can be gained in the HZ without posing unreasonable risk. The main barriers to implementation of HZ considerations include lack of information, absence of established metrics for evaluating success, small number of controlled HZ experiments in urban settings, and concern over risks to both public health and aquatic organisms. A combination of field studies, laboratory experiments, and model development that consider hydrological, chemical, and biological interactions in the HZ can overcome these barriers.
Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.
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