The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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Natural and man-made disasters have been causing destruction and distress to humanity all over the world. In these scenarios, communication infrastructures are the most affected entities making the rescue and emergency response operations extremely challenging. This invokes a need to equip the affected people and the emergency responders with the ability to rapidly set up and use independent means of communication. Therefore, in this work, we present a complete endto-end solution that can connect survivors of a disaster with each other and the authorities using a completely self-sufficient ad hoc network that can be setup rapidly. Accordingly, we develop a Heterogeneous Efficient Low Power Radio (HELPER) that acts as a WiFi (Wireless Fidelity) access point for end-users to connect using website application developed by us. These HELPERs then coordinate with each other to form a LoRa based ad hoc network. To this end, we propose a novel cross-layer optimized distributed energy-efficient routing (SEEK) algorithm that aims to maximize the network lifetime. This aspect is critical especially in energy constrained scenarios after a disaster.To prove the feasibility of the solutions, we prototype the HELPER using WiFi enabled Raspberry Pi and LoRa module that is configured to run using Li-ion batteries. We implement the required cross-layer protocol stack along with the SEEK routing algorithm and develop a website application that an end-user can avail to connect using any device such as smartphones, tablets, laptops etc. We have conducted demonstrations to establish the feasibility of exchanging of text messages over the HELPER network, live map updates, ability to send distress messages (like 9-1-1 calls) to authorities. In the context of authorities, we have shown how they can leverage this technology to remotely monitor the connectivity of the affected area, alert users of imminent dangers and share resource (water, food, first aid) availability information. We have also conducted an extensive numerical evaluation of SEEK algorithm against a greedy geographical routing algorithm using the HELPER testbed. Results showed up to 53% improvement in network lifetime and up to 28% improvement in throughput. Overall, we hope this technology will become instrumental in improving the efficiency and effectiveness of public safety activities.

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