CHIRRUP: a practical algorithm for unsourced multiple access

As an indispensable part of Internet of Things (IoT), wireless sensor networks (WSNs) are more and more widely used with the rapid development of IoT. The neighbor discovery protocols are the premise of communication between nodes and networking in energy-limited self-organizing wireless networks, and play an important role in WSNs. Because the node energy is limited, neighbor discovery must operate in an energy-efficient manner, that is, under the condition of a given energy budget, the neighbor discovery performance should be as good as possible, such that the discovery latency would be as small as possible and the discovered neighbor percentage as large as possible. The indirect neighbor discovery mainly uses the information of the neighbors that have been found by a pairwise discovery method to more efficiently make a re-planning of the discovery wake-up schedules of the original pairwise neighbor discovery, thereby improving the discovery energy efficiency. The current indirect neighbor discovery methods are mainly divided into two categories: one involves removing the inefficient active slots in the original discovery wake-up schedules, and the other involves adding some efficient active slots. However, the two categories of methods have their own limitations. The former does not consider that this removal operation destroys the integrity of the original discovery wake-up schedules and hence the possibility of discovering new neighbors is reduced, which adversely affects the discovered neighbor percentage. For the latter category, there are still inefficient active slots that were not removed in the re-planned wake-up schedules. The motivation of this paper is to combine the advantages of these two types of indirect neighbor discovery methods, that is, to combine the addition of efficient active slots and the removal of inefficient active slots. To achieve this goal, this paper proposes, for the first time, the concept of virtual nodes in neighbor discovery to maximize the integrity of the original wake-up schedules and achieve the goals of adding efficient active slots and removing inefficient active slots. Specifically, a virtual node is a collaborative group that is formed by nodes within a small range. The nodes in a collaborative group share responsibility for the activating task of one member node, and the combination of these nodes’ wake-up schedules forms the full wake-up schedule of a node that only uses a pairwise method. In addition, this paper proposes a set of efficient group management mechanisms, and the key steps affecting energy efficiency are analyzed theoretically to obtain the energy-optimal parameters. The extended simulation experiments in multiple scenarios show that, compared with other methods, our neighbor discovery protocol based on virtual nodes (VN-NDP) has a significant improvement in average discovery delay and discovered neighbor percentage performance at a given energy budget. Compared with the typical indirect neighbor discovery algorithm EQS, a neighbor discovery with extended quorum system, our proposed VN-NDP method reduces the average discovery delay by up to and increases the discovered neighbor percentage by up to .

We present an automated design flow for minimizing the use of diodes and switches (active devices) in design implementations on a nanofabric based on chemically self-assembled electronic nanotechnology as proposed in Goldstein and Budiu [2001]. Connectivity and logic in the nanofabric are realized using the switch and diode behaviors of molecular devices, unlike very large scale integrated (VLSI) circuits where complementary metal-oxide semiconductor (CMOS) gates are used. Similar to the optimization goal of reducing the number of gates in VLSI designs to minimize area, power dissipation, and delay, decreasing the number of switches and diodes used in the nanofabric can potentially minimize design implementation area and power dissipation, besides reducing the delay and signal drop between latched stages in order to improve performance. An integrated placement, topology selection, and routing approach for design implementation on the nanofabric is proposed. Note that this problem is fundamentally different from CMOS VLSI placement and routing because of the inherent routing-dependent logic realization in our target nanofabric. To the best of our knowledge this is the first reported work on automated integrated placement, topology selection, and routing for diode-based nanofabrics. A practical and scalable simulated annealing-based placement and routing algorithm has been implemented. On average, the integrated placement and routing approach achieves a reduction of 12% in the number of switches and diodes used for MCNC benchmarks, compared to separate placement and routing optimization results. The maximum reduction achieved in the number of active devices using our approach is 24%, and in general, we observed that the bigger the benchmark, the larger the improvement achieved.

Homes, offices and vehicles are getting networked. This will enable context aware, autonomous operation of many support systems that could be controlled remotely. To achieve this there would be a large number of tiny devices - sensors and actuators - which are networked and they are termed generally as Internet of Things (IoT) devices. In future, they will be powered through harvested energy from the ambience to enable perennial lifetime and minimal manual maintenance. Some examples of energy sources are photovoltaic panels and piezoelectric crystals. Several challenges arise due to the nature of sources of energy. One of these challenges is that the devices (nodes) leave and re-enter networks due to fluctuating availability of harvested energy. As a result, the neighbor table maintained at each node changes quite often leading to complications in forming and maintaining routes. In fact initial neighbor discovery (ND) itself is a difficult task. Further, usage of directional antennas would increase the time taken to complete ND. We study such a network through exhaustive simulation study considering various parameters. We demonstrate the benefits and challenges of using directional antennas for ND. We present a scheme that nodes could use to discover their neighbor during initial deployment and another scheme that could be used for subsequent discovery on re-entry into the network. We show that a dedicated ND protocol is necessary for energy harvesting networks and that directional ND is beneficial in these networks under some circumstances.

In this paper, we utilize the framework of compressed sensing (CS) for device detection and distributed resource allocation in large-scale machine-to-machine (M2M) communication networks. The devices are partitioned into clusters according to some pre-defined criteria, e.g., proximity or service type. Moreover, by the sparse nature of the event occurrence in M2M communications, the activation pattern of the M2M devices can be formulated as a particular block sparse signal with additional in-block structure in CS based applications. This paper introduces a novel scheme for distributed resource allocation to the M2M devices based on block-CS related techniques, which mainly consists of three phases: (1) In a full-duplex acquisition phase, the network activation pattern is collected in a distributed manner. (2) The base station detects the active clusters and the number of active devices in each cluster, and then assigns a certain amount of resources accordingly. (3) Each active device detects the order of its index among all the active devices in the cluster and accesses the corresponding resource for transmission. The proposed scheme can efficiently reduce the acquisition time with much less computation complexity compared with standard CS algorithms. Finally, extensive simulations confirm the robustness of the proposed scheme under noisy conditions.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

Background/Objectives: Internet of Things (IoT) is going to govern the future world where all devices will be connected with each-other. The expectation with the Internet of Things is that the increase in number of devices going to be truly dramatic. Method: With the increase in the number of devices, the amount of traffic will grow together with data. IoT will have huge number of power sensitive smaller devices and they're going to put significant constraint on the total bandwidth of the system. This paper analyses how 6LoWPAN, a practical developed for resource constrained IP based systems suitable for Internet of Things with respect to Internet Protocol. Findings/Applications: Internet protocols used to be fine-tuned, modified, changed to be able to accommodate the era of Internet of Things. 6LoWPAN has different features like, support for 64 bit or 16-bit addressing, targeted at low power networks including Bluetooth low energy, header compression for IPv base as well as for UDP headers, network auto configuration and neighbor discovery, support for multicast, unicast, and broadcast, supporting the concept of fragmentation. This makes 6LoWPAN a best suited protocol for IoT.

In recent studies, visible light communication (VLC) has been predicted to be a prospective technique in the future 6G communication systems. To suit the trend of exponentially growing connectivity, researchers have intensively studied techniques that enable multiple access (MA) in VLC systems, such as the MIMO system based on LED devices to support potential applications in the Internet of Things (IoT) or edge computing in the next-generation access network. However, their transmission rate is limited due to the intrinsic bandwidth of LED. Unfortunately, the majority of visible light laser communication (VLLC) research with beyond 10 Gb/s data rates concentrates on point-to-point links, or using discrete photodetector (PD) devices instead of an integrated array PD. In this paper, we demonstrated an integrated PD array device fabricated with a Si-substrated GaN/InGaN multiple-quantum-well (MQW) structure, which has a 4×4 array of 50 μm×50 μm micro-PD units with a common cathode and anode. This single-integrated array successfully provides access for two different transmitters simultaneously in the experiment, implementing a 2×2 MIMO-VLLC link at 405 nm. The highest data rate achieved is 13.2 Gb/s, and the corresponding net data rate (NDR) achieved is 12.27 Gb/s after deducing the FEC overhead, using 2.2 GHz bandwidth and superposed PAM signals. Furthermore, we assess the Huffman-coded coding scheme, which brings a fine-grain adjustment in access capacity and enhances the overall data throughput when the user signal power varies drastically due to distance, weather, or other challenges in the channel condition. As far as we know, this is the first demonstration of multiple visible light laser source access based on a single integrated GaN/InGaN receiver module.

In the contemporary era of unprecedented innovations such as Internet of Things (IoT), modern applications cannot be imagined without the presence of Wireless Sensor Network (WSN). Nodes in WSN use neighbour discovery (ND) protocols to have necessary communication among the nodes. Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered. The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots. The two methods are found to have certain drawbacks. The first category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours. When second category is followed, it may have inefficient slots in the wake-up schedules leading to performance degradation. Therefore, the motivation behind the work in this paper is that by combining the two categories, it is possible to reap benefits of both and get rid of the limitations of the both. Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots. Thus a Hybrid Approach to Neighbour Discovery (HAND) protocol is realized in WSN. The simulation study revealed that HAND outperforms the existing indirect ND models.

As one of the three major scenarios of fifth-generation (5G) communication system, massive machine type communications (mMTC) raises more challenges for development of new radio access technology. Unlike the human-to-human communications, the mMTC typically exhibit features such as massive number of devices, small-sized packets and sporadic transmission, which requires novel solutions to satisfy these features. In this paper, we propose a novel grant-free compressive sensing based solution with system level design to jointly conduct the active devices detection, channel estimation and data recovery without knowing the number of active devices. Specifically, in proposed solution, we integrate the sparsity information of channel coefficients and data symbols and exploit the constellation information of modulation and the data diversity of different devices to enhance the performance of receiver. Simulation results demonstrate that the proposed solutions have improve the performance of active device detection, channel estimation, data recovery and the throughput of the system.

Internet of things (IoT) is emerging as a prominent phenomenon in every domain including library and information science. Thus, this study aims to define the internet of things (IoT) concept and further conducts the SWOT analysis of internet of things (IoT) in connection to the library and information science domain in order to know the different insights of the internet of things (IoT). This study may be useful for knowing the different aspects of the internet of things (IoT) phenomena.

The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.

To enhance the service quality of the unmanned aerial vehicle (UAV), the UAV-aided Internet of Things (IoT) systems could deploy a Deep Neural Network (DNN) for performance prediction for the users. Non-orthogonal multiple access (NOMA) is applied to such networks in order to improve spectrum efficiency, and results in improved quality of service at the ground users under the mobility of UAV. The outage and ergodic capacity requirements of the IoT users may not be satisfied due to some imperfect system parameters such as hardware noise. A DNN-based algorithm for performance prediction and the design of multiple antennas at the UAV under hardware noise is proposed. In this DNN-based UAV-NOMA, the central controller (server) collects system parameters periodically based on observing the state of IoT system and performs adjustments to the dynamic environment. The closed-form expressions for the outage probability and the ergodic capacity are derived to evaluate the performance of a group of IoT users. Our numerical results demonstrate that: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i) In contrast to the traditional UAV-NOMA system, the UAV cannot know the performance at each IoT user in order to adjust the parameters (i.e. power allocation factors) before transmitting the signals to the devices; while the proposed DNN-based IoT system is capable of predicting the performance; ii) The performance of the IoT users can be significantly improved by integrating more antennas at the UAV and limiting levels of hardware noise; iii) By designing NOMA, the UAV-NOMA-based IoT system can increase the throughput to the tune of 40% compared with the benchmark (the orthogonal multiple access (OMA)-based IoT)</i> .

We discuss a particular method of random access communications for asynchronous multiple access. We first compare the classical synchronous capacity with the respective asynchronous capacities for three different types of multiuser channels: the OR; the OR with intensity information and the XOR. We assume that the number of active users (inputs) T is large. In particular, we show that for instance, for M-ary input asynchronous access, multiple user interference reduces the capacity for the OR channel, from (M-1) to (ln2)(M-1) bits per transmission. However, for the OR with intensity information the channel capacity reduces from log/sub 2/(T+1) to 0.83 bits per transmission. For the binary input XOR, the capacity reduces from 1 to 0.32 bits per transmission. We conclude with a capacity achieving transmission method for the M-ary input OR channel and compare the presented method with earlier results.

In synchronous code division multiple access (CDMA) systems, both multiple access interference (MAI) and intersymbol interference (ISI) arise. MAI is present due to the CDMA format while ISI is present due to channel multipath. One approach to overcome these problems is to detect the entire package of data for all users at once. This implies working with matrices with dimensions proportional to the product of the data package length and the number of active users. In this paper, we present an alternative approach. This alternative consists of a type of multistage detector that overcomes the MAI and ISI problems; in addition, it has complexity proportional to the number of CDMA users, is computationally efficient, and is suitable for pipeline implementation which allows fast data processing. Simulations are compared to a derived single user bound for the average bit error probability and the results prove the validity of the proposed detection method.

Heating, ventilation and air-conditioning (HVAC) accounts for around 40% of the total building energy con¬sumption. It has therefore become a major target for reductions, in terms of both energy usage and CO2 emissions. In the light of progress in building intelligence and energy technologies, traditional methods for HVAC optimi¬zation, control, and fault diagnosis will struggle to meet essential requirements such as energy efficiency, occu¬pancy comfort and reliable fault detection. Machine learning and data science have great potential in this regard, particularly with developments in information technology and sensor equipment, providing access to large vol¬umes of high-quality data. There remains, however, a number of challenges before machine learning can gain widespread adoption in industry. This review summarizes the recent literature on machine learning for HVAC system optimization, control and fault detection. Unlike other reviews, we provide a comprehensive coverage of the applications, including the factors considered. A brief overview of machine learning and its applications to HVAC is provided, after which we critically appraise the recent literature on control, optimization and fault diagnosis and detection. Finally, we provide a comprehensive discussion on the limitations of current research and suggest future research directions. Internet of things involves interconnected devices and technology facilitating communication between devices and the cloud, as well as among devices themselves. The study is correlational study designed to investigate the role and applications of internet of things in higher education: Uses and ways IoT affects students’ learning. The population of the study is 88 year two students in the department of education, School of Education and Humanities, Babcock University, IIishan and 65 year two students in the department of educational technology, School of computer and Information science, Crawford University, Igbesa. A sample of 100 students was used for the study. A simple random sampling technique was used for the study. The Instrument used for the study was a structured questionnaire titled the role of Internet of Things in Education: Uses and ways IoT affects student’s learning (RITEUWIOTASL) with 30 items. For the study, face and content validity were used. Using Pearson product moment correlation, reliability co-efficient of 0.86 was found. The study's statistical methods included the mean and z test. The study found that IoT devices contribute to campus security, through the integration of surveillance cameras, access control systems and sensors and also IoT facilitates efficient management of educational resources including textbooks, equipment, and digital content. Based on the findings, it was recommended that Educational institutions should invest in comprehensive professional development programs for educators to enhance their understanding of IoT technologies and their effective integration into teaching practices. This will empower educators to leverage IoT tools to create innovative and engaging learning experiences.