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.

Estimating the number of people in a given area and knowing their positions open up numerous possibilities for a variety of smart data driven applications. Most of the existing systems either require active participation from people in the crowd or are too expensive to be deployed. The exponentially increasing adoption of smartphones by people and the ubiquitous Wi-Fi infrastructure motivated us to tackle this problem in a non-intrusive manner. This thesis focuses on designing a system to infer the crowd distribution in large indoor spaces. As we choose to be non-intrusive in approach, we incorporate low cost Wi-Fi sniffers into smart bulbs that are part of the lighting grids in buildings. The Wi-Fi data gathered from these smart bulbs is analyzed to obtain people count and the people’s position within a given area. Our aim is to estimate people’s location within 4x4 m2 grids with minimal number of Wi-Fi frames. A number of filtering and post processing mechanisms are proposed in order to eliminate false positives and to accurately identify the number of people within a given area. Extensive experiments are conducted in a realworld testbed with controlled settings as well as in test setups with no control (auditorium and a coffee corner). The improvised counting algorithm comes close to 75% of the ground truth. We adopt range free localization algorithms to estimate the position of people and evaluate these algorithms extensively. We propose enhancements on these algorithms to refine the position estimation accuracy and reduce the execution time. Range free localization algorithms were able to estimate the position 40% of the time within 2m when sniffers are placed 6m apart in a grid topology in a highly multipath testbed environment. Simulations indicate this number can increase up to 76% when sniffers are placed 4m for the same topology. Since the bulbs with sniffers cost more we minimize the overall deployment cost of the system by reducing the number of sniffers while maintaining an accuracy within a 4x4 m2 grid majority of the time. Extensive simulations are run with different topologies and sniffer densities in order to find this sweet spot.
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