OpenCarrier: Breaking the User Limit for Uplink MU-MIMO Transmissions With Coordinated APs

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices.

In this article, we consider the millimeter-wave simultaneous wireless information and power transfer nonorthogonal multiple access (NOMA) relay networks in the presence of multiple passive eavesdroppers, where the near and far Internet of Things (IoT) devices with different communication requirements are served by one source with the help of the relays, and stochastic geometry is used to characterize the distribution of multiple nodes. First, a NOMA pairing and relay selection scheme is designed. Then, the closed-form expressions for the energy-information coverage probability (EICP) and the effective secrecy throughput of the near IoT devices are derived. Finally, the EICPs of the far IoT device under random relay selection and opportunistic relay selection schemes are obtained. Moreover, the asymptotic expressions of the EICP for both the near and far IoT devices are derived, which show that the asymptotic EICP when the number of antennas at the source or relay tends to infinity is only dependent on the location distribution of nodes and blockage environment. The simulation results show that NOMA can outperform orthogonal multiple access, and the advantage of the opportunistic relay selection scheme over the random relay selection scheme is more obvious when the density of the relay is relatively large.

Many reports predicted that the number of connected IoT (Internet of Things) devices will reach to billions in the next several years, accordingly, how to securely and effectively manage, monitor and control them becomes a critical problem. In conventional IoT solutions, direct SSL/TLS based HTTP connections to IoT devices with high overhead are required and encryption is not considered due to low computing capability and memory capacity of IoT devices. In this paper, we propose an integrated mechanism using DNS (Domain Name System) to accomplish the objective. In the proposed mechanism, names or IDs of IoT devices are managed by DNS server and the monitoring and control are conducted by the collaboration of DNS name resolution, DNS dynamic update and DNS zone transfer. Considering the security and privacy protection, the status and control command for IoT devices described in the corresponding DNS TXT records will be encrypted and TSIG (Transaction SIGnatures) will be used for authentication to restrict the clients allowed to monitor and control the IoT devices.

The continuous rapid growth of Internet of Things (IoT) devices has presented a new model in fourth generation and beyond cellular networks. This continuous growth and the increasing demand in the provision of high transmission rate, delay sensitive and spectrum efficient cellular networks have made the development of fifth generation (5G) networks a reality. The design of the end-to-end 5G networks anticipated the need for terrestrial radio access networks to be complemented by its satellite counterpart in order to ensure that 5G services are provided seamlessly. These 5G services include IoT communications. This has necessitated the need for 5G satellite radio access networks to provide access to IoT devices that are located in remote and rural areas. This type of IoT devices are termed Internet of Remote Things (IoRT) devices. While this remain an appealing solution, many radio resource management issues including packet scheduling for IoRT communications in 5G satellite networks remains undefined. Hence, this paper aims to propose a new dynamic packet scheduling algorithm that will be appropriate for mixed traffic type IoRT communications in 5G satellite networks. The performance evaluation of the proposed packet scheduler is conducted through simulations, using delay, spectral efficiency, throughput and fairness index as the performance indices.

The narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the third-generation partnership project (3GPP) to provide connectivity to a large number of low-cost Internet of Things (IoT) devices with strict energy consumption limitations. However, in an ultradense small cell network employing NB-IoT technology, intercell interference can be a problem, raising serious concerns regarding the performance of NB-IoT, particularly in uplink transmission. Thus, a power allocation method must be established to analyze uplink performance, control and predict intercell interference, and avoid excessive energy waste during transmission. Unfortunately, standard power allocation techniques become inappropriate as their computational complexity grows in an ultradense environment. Furthermore, the performance of NB-IoT is strongly dependent on the traffic generated by IoT devices. In order to tackle these challenges, we provide a consistent and distributed uplink power allocation solution under spatiotemporal fluctuation incorporating NB-IoT features, such as the number of repetitions and the data rate, as well as the IoT device’s energy budget, packet size, and traffic intensity, by leveraging stochastic geometry analysis and mean-field game (MFG) theory. The effectiveness of our approach is illustrated via extensive numerical analysis, and many insightful discussions are presented.

In this study, we first present a framework that jointly optimize energy harvesting and information decoding for Internet of Things (IoT) devices, which are capable of simultaneous wireless information and power reception, in a smarty city. In particular, a generalized power splitting receiver for IoT devices is designed, where each antenna in the receiver has an independent power splitter, unlike the existing works in which only one power splitter is employed regardless of the number of antennas in the receiver. Such a receiver design can provide a great degree of freedom to improve the network performance. Based on the presented framework, for each IoT device, we formulate an optimization problem whose objective is to maximize the harvested energy of each IoT device while satisfying its data rate requirement. To solve this problem, we propose a double-deep deterministic policy gradient based online learning algorithm which enables each IoT device to jointly determine receive beamforming and power splitting ratio vectors in real-time. Further, each IoT device can implement the proposed algorithm in a distributed manner using only its local channel state information. As such, cooperation and information exchange among the base stations and IoT devices are not necessary when performing the proposed algorithm at IoT devices. The extensive simulation results show the validity of the proposed algorithm.

High-density base station (BS) will bring the problem of frequent handover to mobile Internet of Things (IoT) devices. Frequent handover will lead to Quality-of-Service (QoS) problems, such as increased handover delay and reduced data transmission rate. In order to overcome the limitation of mobile IoT devices’ QoS caused by frequent handover BSs, the multiple-input–multiple-output (MIMO) systems can be used to improve the channel capacity and reduce the BS density. However, because the handover decision of mobile IoT devices will affect the data rate and network stability, how to implement the handover to reduce the impact of handover on mobile IoT devices is a problem worthy of study. Therefore, this article proposes a handover strategy based on fuzzy logic in MIMO systems, which are used to reduce the handover frequency and improve the average data rate of mobile IoT devices. First, the mobile IoT devices convert the devices’s speed, the distance from the devices to the MIMO systems, and the transmission power of the MIMO systems to fuzzy values. Then, the fuzzy values obtained in the first step are taken as the input, and the fuzzy-logic-based handover algorithm is implemented through the fuzzy logic controller. Finally, according to the results of the handover algorithm and the theoretical results about the average throughput of mobile IoT devices derived in this article, the number of antennas used to provide transmission services for IoT devices with different data rate requirements and different mobile speeds can be allocated more flexibly. Simulation results show that the proposed scheme achieves a better performance.

Blocking probability of massive MIMO: What is the capacity of a massive MIMO IoT system?

In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT) networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink transmission from IoT devices to base station (BS). Multiple UAVs are employed as UAV relays between IoT devices and BS to enhance received signal strength at BS. Specifically, IoT devices periodically detect wireless channel quality between IoT devices and BS, as well as that among IoT devices. Based on the wireless channel quality, we propose a distributed user cluster (UC) algorithm to cluster IoT devices as multiple UCs. One IoT device in a UC, which is named cluster head (CH), is selected to connect to the BS and gather uplink signals of IoT devices. If the wireless channel quality between CH and BS is good, a direct connection between CH and the BS can be built. Otherwise, UAVs are divided into multiple UAV cooperative relay clusters (CRCs). The UAVs in a CRC are located between a specific CH and BS to relay uplink signals. We then formulate a system optimization model to minimize system power consumption, where UAV deployment and transmission power of UAV are jointly optimized. We solve this optimization problem by dual decomposition method. By extensive simulations, we demonstrate the effectiveness of the proposed algorithm. We also reveal several interesting insights for practical UAV aided IoT networks.

Wireless power transfer (WPT) is an alternative technology to conventional batteries for powering Internet of things (IoT) devices. WPT is especially beneficial in situations when battery replacement is infeasible or expensive. It can also reduce battery-related e-waste. In this paper, we analyze the limits of adopting WPT technology for remote powering of IoT devices. We assume that an IoT device periodically harvests energy from a base station (BS) and transmits a data packet related to the sensor measurement under shadow fading channel conditions. Our goal is to characterize the ε-coverage range, where ε is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal ε-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the weighted sum of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal ε-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the ε-coverage range. Our results suggest that for an IoT device designed using current technology, the maximal 0.95-coverage range is in the order of 120 m. When high measurement rates are required, the coverage range drops to 50–100 m. Compared to battery-powered IoT devices, WPT is well-suited for medium-range applications plus when battery replacement is costly.

Narrowband Internet of Things (NB-IoT) is one of the low-power wide-area network (LPWAN) technologies that aim to support enormous connections, featuring wide-area coverage, low power consumption, and low costs. NB-IoT could serve a massive number of IoT devices, but with very limited radio resources. Therefore, how to enable a massive number of IoT devices to transmit messages periodically, and with low latency, according to transmission requirements, has become the most crucial issue of NB-IoT. Moreover, IoT devices are designed to minimize power consumption so that the device battery can last for a long time. Similarly, the NB-IoT system must configure different power-saving mechanisms for different types of devices to prolong their battery lives. In this study, we propose a persistent periodic uplink scheduling algorithm (PPUSA) to assist a plethora of Internet of Things (IoT) devices in reporting their sensing data based on their sensing characteristics. PPUSA explicitly considers the power-saving mode and connection suspend/resume procedures to reduce the IoT device’s power consumption and processing overhead. PPUSA allocates uplink resource units to IoT devices systematically so that it can support the periodic–uplink transmission of a plethora of IoT devices while maintaining low transmission latency for bursty data. The simulation results show that PPUSA can support up to 600,000 IoT devices when the NB-IoT uplink utilization is 80%. In addition, it takes only one millisecond for the transmission of the bursty messages.

With the wide adoption of Internet of Things (IoT) devices, it becomes crucial to identify which IoT devices are connected to the network at a specific time. Previous studies have built machine learning models that can accurately identify IoT devices on a specific network based on their traffic characteristics. However, one limitation of such models is that whenever a new device joins the network, the model has to be retrained from scratch, which adds a lot of computation overhead. In this work, we propose the use of Siamese Neural Networks to reduce the retraining frequency of IoT device identification models. We use a public dataset containing traffic features from 10 devices. To validate the proposed idea, we first compare the performance of classical multi-class classification neural networks with Siamese Networks on the task at hand. We see that both networks perform similarly. Then, we build 10 separate models based on Siamese networks, and we train each of them to recognize a different combination of 9 devices. Then, we use each of the trained models to recognize the device that was not part of the training set. We assess the performance of each model, and we compare the results with the ones achieved by the multi-class classification network. We prove that with the proposed approach, similar or even better outcomes are achieved, with the main advantage of not having to retrain. Finally, we test the proposed approach against 2 other datasets: Aalto and UNSW. We compare the outcomes with previous works, and we prove that Siamese Networks achieve a better performance.

As internet of things (IoT) devices are booming, a huge amount of data is sleeping without being used. At the same time, reliable and accurate time series analysis plays a key role in modern intelligent systems for achieving efficient management. One reason why the data are not being used is that outliers are preventing many algorithms from working effectively. Manual data cleaning is taking the majority time before one solution could really work on data. Thus, data cleaning, especially fully automated outlier detection is the bottleneck which should be resolved as soon as possible. Previous work has investigated this topic but lacks study on overview from outlier and detection categorization aspects at the same time. This works aims to start covering this topic and to find a direction regarding how to make outlier detection and labelling more automated and general to be suitable for most time series data from IoT devices.

This paper considers the full system verification of a large size CAN network (up to 24 nodes) at high transmission rate (500 Kbps and 1.0 Mbps). The entire CAN physical layer is simulated by using behavior modeling language (VHDL-AMS) in comparison to measurement. The modeling of measurement environment of the CAN network is discussed, showing how to get the measurement and simulation results well matched. This demonstrates that the simulation solution is reliable, which is highly desired and very important for the verification requirement in CAN physical layer design. With the developed CAN measurement system, application layer is also proven working properly when implementing different non-standard topologies at high speed transmission rate.

Cybersecurity is one of the building blocks in need of increasing attention in Internet of things (IoT) applications. IoT has become a popular target for attackers seeking sensitive and personal user data, computing infrastructure for massive attacks, or aimed at compromising critical applications. Worryingly, the industrial race toward the forefront of IoT software and device development has led to increased market penetration of vulnerable IoT devices and applications. Nevertheless, traditional cybersecurity solutions designed for personal computers often rely on heavy computation and high communication overhead, and therefore are prohibitive for IoT, given the explosive number of IoT devices, their resource-con-strained nature, and their heterogeneity. Hence, innovative solutions must be designed for securing IoT applications, while considering the peculiar characteristics of IoT devices and networks. In this article, we discuss the motivations and challenges of using machine learning (ML) models for the design of cybersecurity solutions for IoT. More specifically, we tackle the challenge of designing ML-based solutions and provide guidelines for ML-based physical layer solutions aimed at securing IoT. We propose a device-oriented and network-oriented classification and investigate recent works that designed ML-based solutions, considering IoT physical layer features, to secure IoT applications. The proposed classification helps engineers and practitioners starting in this area to better identify and understand the challenges, requirements, and up-to-date common design principles for securing IoT devices and networks considering physical layer features. Finally, we shed light on some future research directions that need further investigation.