Design implementation and analysis of wireless model based predictive networked control system over cooperative wireless network

Owing to their distributed architecture, networked control systems are proven to be feasible in scenarios where a spatially distributed control system is required. Traditionally, such networked control systems operate over real-time wired networks over which sensors, controllers and actuators interact with each other. Recently, in order to achieve the utmost flexibility, scalability, ease of deployment and maintainability, wireless networks such as IEEE 802.11 LANs are being preferred over dedicated wired networks. However, basic networked control systems cannot operate over such general purpose wireless networks since the stability of the system is compromised due to unbounded delays and unpredictable packet losses that are typical in the wireless medium. Approaching the wireless networked control problem from two perspectives, this thesis proposes a novel wireless networked control system and a realistic cooperative medium access control protocol implementation that work jointly to achieve decent control even under unbounded delay, bursts of packet loss and ambient wireless traffic. The proposed system is implemented and thoroughly evaluated on a dedicated test platform under numerous scenarios and is shown to be operational under bursts of packet loss and ambient wireless traffic levels which are intolerable for basic networked control systems while not being hindered by restraining assumptions of existing methods

Channel prediction in wireless communications

Knowledge of the channel over which signals are sent is of prime importance in modern wireless communications. Inaccurate or incomplete channel information leads to high error rates and wasted bandwidth and energy. Although active channel measurement is commonly used to gain channel knowledge, it can only accurately represent the channel at the time the measurement was taken, makes energy and bandwidth demands, and adds significant complexity to the radio system. Due to the highly time variant nature of wireless channels, active measurements become invalid almost as soon as they are taken, making alternative approaches to predicting future behaviour highly attractive. Such systems would allow maximum advantage to be taken of the limited bandwidth available and make significant power savings. This thesis investigates a number of complementary technologies, leading towards a channel prediction scheme suitable for mobile devices. As a first step towards channel prediction, anomaly detection is investigated within periodic wireless signals to establish when radical changes in the channel occur. In pre- vious experiments, long monotonic sequences had been observed to coincide with certain anomalies but not others when using Kullback-Leibler Divergence (KLD) analysis, possibly allowing the characterisation of anomaly types. An investigation is described to explain the origin of these features in a rigorous mathematical sense. A proof is given for the causes of the monotonic sequences, followed by a discussion of the types of signal anomaly which would underly such a feature and the value of this information. The second part describes a novel channel characterisation method which uses a class of Recurrent Neural Network (RNN) called an Echo State Network (ESN). Using this tool, a channel characterisation system can be constructed without an explicit statistical or mathematical model of the wireless environment, relying instead on observed data. This approach is much more convenient than existing models which require detailed information about the wireless system's parameters and also allows for new channel classifications to be added easily. It is able to achieve double the correct classification rate of a conventional statistical classifier, and is computationally simple to implement, making it ideal for inclusion on low-power mobile devices. Following their successful use in characterisation, ESNs are used in the final part in an investigation into channel prediction in a number of different scenarios. They were however found to be unable to produce useful predictions for all but the most trivial channel models. An alternative method is described for indoor environments using an approach inspired by ray tracing. It is simple and computationally lightweight to implement, again making it suitable for mobile devices. Simulation results show that it can outperform pilot-assisted methods by a significant margin, while not wasting bandwidth on channel measurement

Decentralized Ultra-Reliable Low-Latency Communications through Concurrent Cooperative Transmission

Emerging cyber-physical systems demand for communication technologies that enable seamless interactions between humans and physical objects in a shared environment. This thesis proposes decentralized URLLC (dURLLC) as a new communication paradigm that allows the nodes in a wireless multi-hop network (WMN) to disseminate data quickly, reliably and without using a centralized infrastructure. To enable the dURLLC paradigm, this thesis explores the practical feasibility of concurrent cooperative transmission (CCT) with orthogonal frequency-division multiplexing (OFDM). CCT allows for an efficient utilization of the medium by leveraging interference instead of trying to avoid collisions. CCT-based network flooding disseminates data in a WMN through a reception-triggered low-level medium access control (MAC). OFDM provides high data rates by using a large bandwidth, resulting in a short transmission duration for a given amount of data. This thesis explores CCT-based network flooding with the OFDM-based IEEE 802.11 Non-HT and HT physical layers (PHYs) to enable interactions with commercial devices. An analysis of CCT with the IEEE 802.11 Non-HT PHY investigates the combined effects of the phase offset (PO), the carrier frequency offset (CFO) and the time offset (TO) between concurrent transmitters, as well as the elapsed time. The analytical results of the decodability of a CCT are validated in simulations and in testbed experiments with Wireless Open Access Research Platform (WARP) v3 software-defined radios (SDRs). CCT with coherent interference (CI) is the primary approach of this thesis. Two prototypes for CCT with CI are presented that feature mechanisms for precise synchronization in time and frequency. One prototype is based on the WARP v3 and its IEEE 802.11 reference design, whereas the other prototype is created through firmware modifications of the Asus RT-AC86U wireless router. Both prototypes are employed in testbed experiments in which two groups of nodes generate successive CCTs in a ping-pong fashion to emulate flooding processes with a very large number of hops. The nodes stay synchronized in experiments with 10 000 successive CCTs for various modulation and coding scheme (MCS) indices and MAC service data unit (MSDU) sizes. The URLLC requirement of delivering a 32-byte MSDU with a reliability of 99.999 % and with a latency of 1 ms is assessed in experiments with 1 000 000 CCTs, while the reliability is approximated by means of the frame reception rate (FRR). An FRR of at least 99.999 % is achieved at PHY data rates of up to 48 Mbit/s under line-of-sight (LOS) conditions and at PHY data rates of up to 12 Mbit/s under non-line-of-sight (NLOS) conditions on a 20 MHz wide channel, while the latency per hop is 48.2 µs and 80.2 µs, respectively. With four multiple input multiple output (MIMO) spatial streams on a 40 MHz wide channel, a LOS receiver achieves an FRR of 99.5 % at a PHY data rate of 324 Mbit/s. For CCT with incoherent interference, this thesis proposes equalization with time-variant zero-forcing (TVZF) and presents a TVZF receiver for the IEEE 802.11 Non-HT PHY, achieving an FRR of up to 92 % for CCTs from three unsyntonized commercial devices. As CCT-based network flooding allows for an implicit time synchronization of all nodes, a reception-triggered low-level MAC and a reservation-based high-level MAC may in combination support various applications and scenarios under the dURLLC paradigm

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

Computational Intelligence for Cooperative Swarm Control

Over the last few decades, swarm intelligence (SI) has shown significant benefits in many practical applications. Real-world applications of swarm intelligence include disaster response and wildlife conservation. Swarm robots can collaborate to search for survivors, locate victims, and assess damage in hazardous environments during an earthquake or natural disaster. They can coordinate their movements and share data in real-time to increase their efficiency and effectiveness while guiding the survivors. In addition to tracking animal movements and behaviour, robots can guide animals to or away from specific areas. Sheep herding is a significant source of income in Australia that could be significantly enhanced if the human shepherd could be supported by single or multiple robots. Although the shepherding framework has become a popular SI mechanism, where a leading agent (sheepdog) controls a swarm of agents (sheep) to complete a task, controlling a swarm of agents is still not a trivial task, especially in the presence of some practical constraints. For example, most of the existing shepherding literature assumes that each swarm member has an unlimited sensing range to recognise all other members’ locations. However, this is not practical for physical systems. In addition, current approaches do not consider shepherding as a distributed system where an agent, namely a central unit, may observe the environment and commu- nicate with the shepherd to guide the swarm. However, this brings another hurdle when noisy communication channels between the central unit and the shepherd af- fect the success of the mission. Also, the literature lacks shepherding models that can cope with dynamic communication systems. Therefore, this thesis aims to design a multi-agent learning system for effective shepherding control systems in a partially observable environment under communication constraints. To achieve this goal, the thesis first introduces a new methodology to guide agents whose sensing range is limited. In this thesis, the sheep are modelled as an induced network to represent the sheep’s sensing range and propose a geometric method for finding a shepherd-impacted subset of sheep. The proposed swarm optimal herding point uses a particle swarm optimiser and a clustering mechanism to find the sheepdog’s near-optimal herding location while considering flock cohesion. Then, an improved version of the algorithm (named swarm optimal modified centroid push) is proposed to estimate the sheepdog’s intermediate waypoints to the herding point considering the sheep cohesion. The approaches outperform existing shepherding methods in reducing task time and increasing the success rate for herding. Next, to improve shepherding in noisy communication channels, this thesis pro- poses a collaborative learning-based method to enhance communication between the central unit and the herding agent. The proposed independent pre-training collab- orative learning technique decreases the transmission mean square error by half in 10% of the training time compared to existing approaches. The algorithm is then ex- tended so that the sheepdog can read the modulated herding points from the central unit. The results demonstrate the efficiency of the new technique in time-varying noisy channels. Finally, the central unit is modelled as a mobile agent to lower the time-varying noise caused by the sheepdog’s motion during the task. So, I propose a Q-learning- based incremental search to increase transmission success between the shepherd and the central unit. In addition, two unique reward functions are presented to ensure swarm guidance success with minimal energy consumption. The results demonstrate an increase in the success rate for shepherding