For the Internet of Things (IoT) applications that send a few bytes of sensor information infrequently, several long-range IoT technologies have been conceived. Narrowband IoT (NB-IoT) is one of them that stands out due to its extended coverage, high penetrability, and high reliability features. Envisaged
long device lifetime and the extended coverage are important aspects of the NB-IoT radio technology that has garnered attention. With the increase in applications to connect a large number of devices over long distances, it becomes crucial for the technology to meet the lifetime expectations set in the standards. However, the empirical lifetime estimations, done as part of this work, indicate that the current devices do notmeet the lifetime estimates targeted in the 3GPP standards, lasting for 2.38 yearswhen exchanging 200 bytes of data at a rate of 1 message per day. Potential solutions include
energy-harvesting to augment the battery lifetime. However, due to the spatiotemporal variation in the amount of energy harvested, the supplemented energy levels will be less than the energy consumption. Therefore, it becomes essential for the sensor device enabled with NB-IoT communication technology to implement energy management techniques to maximize the utility to the application it serves based on its energy situation. Further, the coverage enhancement techniques inbuilt in the technology forms a major contributor to the low device lifetime. With the NB-IoT radio block considered as a ‘black box’ with no access to the software stack, innovative solutions are required to reduce
the energy consumption of sending payloads without losing out on coverage.
To this end, this work proposes a framework called xTEND to increase the utility of the device while being energy-efficient in deep coverage scenarios. In xTEND, the decision to transmit a payload is done by modeling the problem as a 0/1 incremental knapsack problem. A threshold policy is proposed that optimally schedules payloads in a given finite horizon, with a low complexity method to compute it on the device. Furthermore, as the coverage enhancement techniques deplete the battery soon for deep coverage scenarios, we propose to increase the transmission power adaptively based on the channel conditions. The framework is evaluated with an existing scheme in the literature that
performs energy management and is shown to outperform in different traffic distributions. Further, with the framework, an average 3.8 J of energy savings and 8.027 s of time savings is obtained in the shared and control channels of the physical layer. This results in an average lifetime extension of 20 % in deep coverage scenarios. With the only requirement of access to the power amplifier embedded on the NB-IoT chipset, the framework can be implemented on the deployed sensor device.

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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