Analysis and Design of Communication Policies for Energy-Constrained Machine-Type Devices

This thesis focuses on the modelling, analysis and design of novel communication strategies for wireless machine-type communication (MTC) systems to realize the notion of Internet of things (IoT). We consider sensor based MTC devices which acquire physical information from the environment and transmit it to a base station (BS) while satisfying application specific quality-of-service (QoS) requirements. Due to the wireless and unattended operation, these MTC devices are mostly battery-operated and are severely energy-constrained. In addition, MTC systems require low-latency, perpetual operation, massive-access, etc. Motivated by these critical requirements, this thesis proposes optimal data communication policies for four different network scenarios. In the first two scenarios, each MTC device transmits data on a dedicated orthogonal channel and either (i) possess an initially fully charged battery of finite capacity, or (ii) possess the ability to harvest energy and store it in a battery of finite capacity. In the other two scenarios, all MTC devices share a single channel and are either (iii) allocated individual non-overlapping transmission times, or (iv) randomly transmit data on predefined time slots. The proposed novel techniques and insights gained from this thesis aim to better utilize the limited energy resources of machine-type devices in order to effectively serve the future wireless networks. Firstly, we consider a sensor based MTC device communicates with a BS, and devise optimal data compression and transmission policies with an objective to prolong the device-lifetime. We formulate joint optimization problems aiming to maximize the device-lifetime whilst satisfying the delay and bit-error-rate constraints. Our results show significant improvement in device-lifetime. Importantly, the gain is most profound in the low latency regime. Secondly, we consider a sensor based MTC device that is served by a hybrid BS which wirelessly transfers power to the device and receives data transmission from the device. The MTC device employs data compression in order to reduce the energy cost of data transmission. Thus, we propose to jointly optimize the harvesting-time, compression and transmission design, to minimize the energy cost of the system under given delay constraint. The proposed scheme reduces energy consumption up to 19% when data compression is employed. Thirdly, we consider multiple MTC devices transmit data to a BS following the time division multiple access (TDMA). Conventionally, the energy-efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission time allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., does not have any impact on the energy-efficiency. We consider that data compression is performed before transmission. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate. Our results show that multi-user sequence optimization achieves up to 45% improvement in the energy-efficiency at MTC devices. Lastly, we consider contention resolution diversity slotted ALOHA (CRDSA) with transmit power diversity where each packet copy from a device is transmitted at a randomly selected power level. It results in inter-slot received power diversity, which is exploited by employing a signal-to-interference-plus-noise ratio based successive interference cancellation (SIC) receiver. We propose a message passing algorithm to model the SIC decoding and formulate an optimization problem to determine the optimal transmit power distribution subject to energy constraints. We show that the proposed strategy provides up to 88% system load performance improvement for massive-MTC systems

Development of a drone-based miniaturized payload for LoRa communications experiment proof-of-concept

The remote sensing and interference detector with RadIometry and vegeTation Analysis (RITA), is a payload of 1U that will fly onboard Alainsat-1 a 3U CubeSat. Among other tests and experiments, it will perform a proof of concept of a LoRa custom module for space-to-Earth communications between the satellite and a terrestrial network of Internet of Things sensors. The LoRa communications experiment proof-of-concept has been performed using several ground IoT nodes and a miniaturized drone-based payload. The communications have been performed using two MAC protocols which are compatible with an IoT scenario: pure ALOHA and CSMA/CA with RTS/CTS. In both protocols, the useful information to be sent is the data contained in the Data Packet. In this packet, the data obtained by the capacitive soil moisture sensor and the temperature sensor are stored. In order to perform LoRa communications experiment proof-of-concept, two measurement campaigns have been realized. In the first measurement campaign, the correct functioning of the LoRa modules and sensors has been tested. In the second measurement campaign, several experiments have been performed in which pure ALOHA and CSMA/CA protocols have been tested. In order to test different experiments with different configurations of the protocols, a general code structure has been designed where both the ground nodes and the drone payload are controlled by a command sent by the user. Therefore, the choice of the protocol to be used as well as the configurable parameters of each protocol are sent through a remote command. Finally, the results obtained in both protocols are analyzed and it is concluded which of the two has better performance against an IoT communications scenario