Goodbye, ALOHA!

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The vision of the Internet of Things (IoT) to interconnect and Internet-connect everyday people, objects, and machines poses new challenges in the design of wireless communication networks. The design of medium access control (MAC) protocols has been traditionally an intense area of research due to their high impact on the overall performance of wireless communications. The majority of research activities in this field deal with different variations of protocols somehow based on ALOHA, either with or without listen before talk, i.e., carrier sensing multiple access. These protocols operate well under low traffic loads and low number of simultaneous devices. However, they suffer from congestion as the traffic load and the number of devices increase. For this reason, unless revisited, the MAC layer can become a bottleneck for the success of the IoT. In this paper, we provide an overview of the existing MAC solutions for the IoT, describing current limitations and envisioned challenges for the near future. Motivated by those, we identify a family of simple algorithms based on distributed queueing (DQ), which can operate for an infinite number of devices generating any traffic load and pattern. A description of the DQ mechanism is provided and most relevant existing studies of DQ applied in different scenarios are described in this paper. In addition, we provide a novel performance evaluation of DQ when applied for the IoT. Finally, a description of the very first demo of DQ for its use in the IoT is also included in this paper.Peer ReviewedPostprint (author's final draft

Energy efficiency in short and wide-area IoT technologies—A survey

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions

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The development and deployment of radio frequency identification (RFID) systems render a novel distributed sensor network which enhances visibility into manufacturing processes. In RFID systems, the detection range and read rates will suffer from interference among high-power reading devices. This problem grows severely and degrades system performance in dense RFID networks. Consequently, medium access protocols (MAC) protocols are needed for such networks to assess and provide access to the channel so that tags can be read accurately. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a desired read rate. The power control scheme and MAC protocol dynamically adjust the RFID reader power output in response to the interference level seen during tag reading and acceptable signal-to-noise ratio (SNR). We present novel distributed adaptive power control (DAPC) as a possible solution. A suitable back off scheme is also added with DAPC to improve coverage. A generic UHF wireless testbed is built using UMR/SLU GEN4-SSN for implementing the protocol. Both the methodology and hardware implementation of the schemes are presented, compared, and discussed. The results of hardware implementation illustrate that the protocol performs satisfactorily as expected

An energy-efficient routing protocol for Hybrid-RFID Sensor Network

Radio Frequency Identification (RFID) systems facilitate detection and identification of objects that are not easily detectable or distinguishable. However, they do not provide information about the condition of the objects they detect. Wireless sensor networks (WSNs), on the other hand provide information about the condition of the objects as well as the environment. The integration of these two technologies results in a new type of smart network where RFID-based components are combined with sensors. This research proposes an integration technique that combines conventional wireless sensor nodes, sensor-tags, hybrid RFID-sensor nodes and a base station into a smart network named Hybrid RFID-Sensor Network (HRSN)

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We anticipate that there will be an enormous amount of wireless devices connected to the Internet through the future-generation wireless networks. Those wireless devices vary from self-driving vehicles to smart wearable devices and intelligent house- hold electrical appliances. Under such circumstances, the network resource optimization faces the challenge of the requirement of both flexibility and performance. Current wireless communication still relies on one-size-fits-all optimization algorithms, which require meticulous design and elaborate maintenance, thus not flexible and cannot meet the growing requirements well. The future-generation wireless networks should be “smarter”, which means that the artificial intelligence-driven software-level design will play a more significant role in network optimization. In this thesis, we present three different ways of leveraging artificial intelligence (AI) and machine learning (ML) to design network optimization algorithms for three wireless Internet of things network optimization problems. Our ML-based approaches cover the use of multi-layer feed-forward artificial neural network and the graph convolutional network as the core of our AI decision-makers. The learning methods are supervised learning (for static decision-making) and reinforcement learning (for dynamic decision-making). We demonstrate the viability of applying ML in future- generation wireless network optimizations through extensive simulations. We summarize our discovery on the advantage of using ML in wireless network optimizations as the following three aspects: 1. Enabling the distributed decision-making to achieve the performance that near a centralized solution, without the requirement of multi-hop information; 2. Tackling with dynamic optimization through distributed self-learning decision- making agents, instead of designing a sophisticated optimization algorithm; 3. Reducing the time used in optimizing the solution of a combinatorial optimization problem. We envision that in the foreseeable future, AI and ML could help network service designers and operators to improve the network quality of experience swiftly and less expensively