Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Uncoordinated access schemes for the IoT: approaches, regulations, and performance

Internet of Things (IoT) devices communicate using a variety of protocols, differing in many aspects, with the channel access method being one of the most important. Most of the transmission technologies explicitly designed for IoT and Machine-to-Machine (M2M) communication use either an ALOHA-based channel access or some type of Listen Before Talk (LBT) strategy, based on carrier sensing. In this paper, we provide a comparative overview of the uncoordinated channel access methods for IoT technologies, namely ALOHA-based and LBT schemes, in relation with the ETSI and FCC regulatory frameworks. Furthermore, we provide a performance comparison of these access schemes, both in terms of successful transmissions and energy efficiency, in a typical IoT deployment. Results show that LBT is effective in reducing inter-node interference even for long-range transmissions, though the energy efficiency can be lower than that provided by ALOHA methods. The adoption of rate-adaptation schemes, furthermore, lowers the energy consumption while improving the fairness among nodes at different distances from the receiver. Coexistence issues are also investigated, showing that in massive deployments LBT is severely affected by the presence of ALOHA devices in the same area

Internet of Things: The next evolutionary step- A Review

Now-a-days the world is witnessing the start of a new era of Internet of Things (IoT) also known as Internet of Objects. In this era, computing will be outside the realm of the traditional desktop and many of the objects surrounding us will be on the network in one form or another. Generally speaking IoT refers to the networked interconnection of everyday objects, which are often equipped with ubiquitous intelligence i.e. we can say that IoT stands for virtually interconnected objects that are identifiable and equipped with sensing, computing, and communication capabilities.IoT promises a great future for the internet where the type of communication is machine-to-machine (M2M). This review presents a vision for worldwide implementation of IoT while also discussing the key enabling technologies and application domains that are likely to drive IoT research in the near future

Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild

Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open-source along with a lightweight data visualization platform.Comment: Accepted for publication in IEEE Communications Magazine (Internet of Things and Sensor Networks Series

An end-to-end LwM2M-based communication architecture for multimodal NB-IoT/BLE devices

The wireless Internet of Things (IoT) landscape is quite diverse. For instance, Low-Power Wide-Area Network (LPWAN) technologies offer low data rate communication over long distance, whereas Wireless Personal Area Network (WPAN) technologies can reach higher data rates, but with a reduced range. For simple IoT applications, communication requirements can be fulfilled by a single technology. However, the requirements of more demanding IoT use cases can vary over time and with the type of data being exchanged. This is pushing the design towards multimodal approaches, where different wireless IoT technologies are combined and the most appropriate one is used as per the need. This paper considers the combination of Narrow Band IoT (NB-IoT) and Bluetooth Low Energy (BLE) as communication options for an IoT device that is running a Lightweight Machine to Machine/Constrained Application Protocol (LwM2M/CoAP) protocol stack. It analyses the challenges incurred by different protocol stack options, such as different transfer modes (IP versus non-IP), the use of Static Context Header Compression (SCHC) techniques, and Datagram Transport Layer Security (DTLS) security modes, and discusses the impact of handover between both communication technologies. A suitable end-to-end architecture for the targeted multimodal communication is presented. Using a prototype implementation of this architecture, an in-depth assessment of handover and its resulting latency is performed

Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.Comment: Best Student Paper Awar

Positioning for the Internet of Things: A 3GPP Perspective

Many use cases in the Internet of Things (IoT) will require or benefit from location information, making positioning a vital dimension of the IoT. The 3rd Generation Partnership Project (3GPP) has dedicated a significant effort during its Release 14 to enhance positioning support for its IoT technologies to further improve the 3GPP-based IoT eco-system. In this article, we identify the design challenges of positioning support in Long-Term Evolution Machine Type Communication (LTE-M) and Narrowband IoT (NB-IoT), and overview the 3GPP's work in enhancing the positioning support for LTE-M and NB-IoT. We focus on Observed Time Difference of Arrival (OTDOA), which is a downlink based positioning method. We provide an overview of the OTDOA architecture and protocols, summarize the designs of OTDOA positioning reference signals, and present simulation results to illustrate the positioning performance.Comment: 8 pages; 7 figures; 1 table; submitted for publicatio

5G NB-IoT via Low Density LEO Constellations

5G NB-IoT is seen as a key technology for providing truly ubiquitous, global 5G coverage (1.000.000 devices/km2) for machine type communications in the internet of things. A non-terrestrial network (NTN) variant of NB-IoT is being standardized in the 3GPP, which along with inexpensive and non-complex chip-sets enables the production of competitively priced IoT devices with truly global coverage. NB-IoT allows for narrowband single carrier transmissions in the uplink, which improves the uplink link-budget by as much as 16.8 dB over the 180 [kHz] downlink. This allows for a long range sufficient for ground to low earth orbit (LEO) communication without the need for complex and expensive antennas in the IoT devices. In this paper the feasibility of 5G NB-IoT in the context of low-density constellations of small-satellites carrying base-stations in LEO is analyzed and required adaptations to NB-IoT are discussed