Tactile internet promises to enable humans to manipulate remote physical environments. The human interacts with the remote environment by manipulating a haptic device. Recently, research effort into tactile internet and haptic devices has seen a significant increase. However replicating one aspect of executing an action, feeling textures, is still in its infancy. Current devices that can portray texture are severely limited, which also interferes with the ability to research the topic. In this thesis, we propose a novel device that can realistically portray textures to the fingertip, and respond to displacement in real-time. We call this a Haptic Texture Device (HTD). The design features custom actuators, with low latency < 0.6ms and at least 50mN per mm2 of pushing force. The actuators are placed in a grid with a 2.0mmpitch at the fingertip. TheHaptic TextureDevice (HTD) can be moved over a 2D surface. Movement of the HTD results in real-time updates to the portrayed texture to reflect the displacement. The end-to-end delay between displacement and update of the texture was measured to be 17 ms. However, we demonstrated that the theoretical limit of the end-to-end delay could bewell below1 ms, by making use of higher quality displacement sensor. Readily available heightmaps are used as a source of texture data. Because a method for characterizing the performance hitherto did not exist, a novel benchmark was developed that incorporates realistic textures and human participants. The results of our benchmarking yielded an average score of 61%. Since the device is the first of its kind, it cannot be compared to similar devices yet, but it can be used as a starting point on which future HTDs can improve.

Human Activity Recognition (HAR) is a key enabler of various applications, including smart homes, health care, Internet of Things (IoT), and virtual reality games. A large number of HAR systems are based on wearable sensors and computer vision. However, a challenge that has emerged in the last few years entails recognizing human activities using WiFi Channel State Information (CSI). Exiting state-of-the-art solutions have considered only amplitudes of the CSI to recognize human activities, we explore both amplitudes and phase differences to recognize activities. We utilize Continuous Wavelet Transform (CWT) to generate scalogram images from the CSI measurements. Then, we use these images as input to the pertained Convolution Neural Network (CNN), namely AlexNet to extract features that are resilient to environment changes and classify the activities. The experimental results show that the proposed method achieves an accuracy of 98.18% +/- 1.26% using amplitude and phase difference. We also studied the impact of different environments and people, and the results show its robustness.

Kitchen is becoming a hotbed for innovation in the Internet of Things (IoT) revolution. Many kitchen appliances are being connected to the Internet to facilitate `smart-cooking'. The appliances are becoming cordless too, i.e., they are being powered by the inductive power sources which are integrated into the kitchen counter-tops. The Wireless Power Consortium (WPC) has proposed standards for smart-cooking in cordless kitchens by enabling communication using the near field communication (NFC) protocol between the appliance and the power transmitter. In order to keep the appliances safe as well as reduce the cost of the appliances, it is required that the NFC channel should be exploited to enable Internet connectivity in the appliances. However, due to practical constraints, the NFC channel is time-slotted. Furthermore, this NFC channel has low data rates and high latencies. These constraints make it highly challenging to enable Internet connectivity for these resource-constrained cooking appliances for IoT applications.

This thesis explores different ways of providing Internet connectivity to the cordless kitchen appliances using the time-slotted NFC channel. Two architectures are proposed based on this method, namely the Proxy and the Bridge architectures. In the proxy architecture, the cordless appliances implement only the application layer and tunnel the application data through the NFC channel which will then be used by the power source to create TCP/IP packets for the appliance. In the bridge architecture, the appliances implement all the layers of the TCP/IP network stack. All the TCP/IP traffic is sent through the NFC channel and the power source acts as an intermediate hop. These architectures are evaluated in detail to determine the best-suited architecture. The thesis concludes that the bridge architecture, although heavy on the appliances, truly creates an IoT-enabled appliance, and therefore adopts it.

While it is proposed to send the complete TCP/IP packets to go over the NFC
channel, the impact on the performance of the protocols needs to be investigated, specifically the TCP as it is the most used protocol for IoT applications. The performance of the TCP will be affected due to several reasons: (a) the time-slotted NFC channel; (b) low data rates on the NFC; (c) delays in accessing the NFC channel, and (d) no control over the network stack of the other TCP end-point. Furthermore, the behavior of the TCP in such resource-constrained channels aggravate the problems as spurious retransmissions get triggered. This work presents important challenges that need to be solved in order to enable the TCP to work smoothly in the time-slotted NFC channels. Two major performance problems that occur in such an environment are identified, viz., spurious retransmissions and packet drops at the NFC interface. The existence of the problems are verified with an experimental setup of the cordless kitchen and solutions are presented to these challenges: (a) determine the optimal retransmission timeout and the heuristic, and (b) avoid packet drops due to small inter-packet delay on the NFC channel. Next, a detailed parametric analysis of the other TCP parameters such as contention window size and maximum segment size of the TCP packets is performed.

From the evaluation, it is found that the proposed solutions can almost completely eliminate spurious retransmissions. With these solutions up to 38% reduction in the system latency is achieved at an NFC bit rate of 11.2 kbps and up to 53% at 24 kbps in the time-slotted mode. By implementing these solutions and choosing the right parameter values for the TCP, it is possible to seamlessly adapt and use the TCP for the time-slotted and resource-constrained NFC channel, and enable a truly IoT-based cooking experience for the smart cordless kitchens.