Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach

In this paper, the problem of user association and resource allocation is studied for an integrated satellite-drone network (ISDN). In the considered model, drone base stations (DBSs) provide downlink connectivity, supplementally, to ground users whose demand cannot be satisfied by terrestrial small cell base stations (SBSs). Meanwhile, a satellite system and a set of terrestrial macrocell base stations (MBSs) are used to provide resources for backhaul connectivity for both DBSs and SBSs. For this scenario, one must jointly consider resource management over satellite-DBS/SBS backhaul links, MBS-DBS/SBS terrestrial backhaul links, and DBS/SBS-user radio access links as well as user association with DBSs and SBSs. This joint user association and resource allocation problem is modeled using a competitive market setting in which the transmission data is considered as a good that is being exchanged between users, DBSs, and SBSs that act as "buyers", and DBSs, SBSs, MBSs, and the satellite that act as "sellers". In this market, the quality-of-service (QoS) is used to capture the quality of the data transmission (defined as good), while the energy consumption the buyers use for data transmission is the cost of exchanging a good. According to the quality of goods, sellers in the market propose quotations to the buyers to sell their goods, while the buyers purchase the goods based on the quotation. The buyers profit from the difference between the earned QoS and the charged price, while the sellers profit from the difference between earned price and the energy spent for data transmission. The buyers and sellers in the market seek to reach a Walrasian equilibrium, at which all the goods are sold, and each of the devices' profit is maximized. A heavy ball based iterative algorithm is proposed to compute the Walrasian equilibrium of the formulated market

Augmented Human Machine Intelligence for Distributed Inference

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and solve problems. Efficient fusion of information from both human and sensor sources is expected to improve system performance and enhance situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves. In this dissertation, we consider that people often have a number of biases and rely on heuristics when exposed to different kinds of uncertainties, e.g., limited information versus unreliable information. We develop novel theoretical frameworks for collaborative decision making in complex environments when the observers may include both humans and physics-based sensors. We address fundamental concerns such as uncertainties, cognitive biases in human decision making and derive human decision rules in binary decision making. We model the decision-making by generic humans working in complex networked environments that feature uncertainties, and develop new approaches and frameworks facilitating collaborative human decision making and cognitive multi-modal fusion. The first part of this dissertation exploits the behavioral economics concept Prospect Theory to study the behavior of human binary decision making under cognitive biases. Several decision making systems involving humans\u27 participation are discussed, and we show the impact of human cognitive biases on the decision making performance. We analyze how heterogeneity could affect the performance of collaborative human decision making in the presence of complex correlation relationships among the behavior of humans and design the human selection strategy at the population level. Next, we employ Prospect Theory to model the rationality of humans and accurately characterize their behaviors in answering binary questions. We design a weighted majority voting rule to solve classification problems via crowdsourcing while considering that the crowd may include some spammers. We also propose a novel sequential task ordering algorithm to improve system performance for classification in crowdsourcing composed of unreliable human workers. In the second part of the dissertation, we study the behavior of cognitive memory limited humans in binary decision making and develop efficient approaches to help memory constrained humans make better decisions. We show that the order in which information is presented to the humans impacts their decision making performance. Next, we consider the selfish behavior of humans and construct a unified incentive mechanism for IoT based inference systems while addressing the selfish concerns of the participants. We derive the optimal amount of energy that a selfish sensor involved in the signal detection task must spend in order to maximize a certain utility function, in the presence of buyers who value the result of signal detection carried out by the sensor. Finally, we design a human-machine collaboration framework that blends both machine observations and human expertise to solve binary hypothesis testing problems semi-autonomously. In networks featuring human-machine teaming/collaboration, it is critical to coordinate and synthesize the operations of the humans and machines (e.g., robots and physical sensors). Machine measurements affect human behaviors, actions, and decisions. Human behavior defines the optimal decision-making algorithm for human-machine networks. In today\u27s era of artificial intelligence, we not only aim to exploit augmented human-machine intelligence to ensure accurate decision making; but also expand intelligent systems so as to assist and improve such intelligence

Research on efficiency and privacy issues in wireless communication

Wireless spectrum is a limited resource that must be used efficiently. It is also a broadcast medium, hence, additional procedures are required to maintain communication over the wireless spectrum private. In this thesis, we investigate three key issues related to efficient use and privacy of wireless spectrum use. First, we propose GAVEL, a truthful short-term auction mechanism that enables efficient use of the wireless spectrum through the licensed shared access model. Second, we propose CPRecycle, an improved Orthogonal Frequency Division Multiplexing (OFDM) receiver that retrieves useful information from the cyclic prefix for interference mitigation thus improving spectral efficiency. Third and finally, we propose WiFi Glass, an attack vector on home WiFi networks to infer private information about home occupants. First we consider, spectrum auctions. Existing short-term spectrum auctions do not satisfy all the features required for a heterogeneous spectrum market. We discover that this is due to the underlying auction format, the sealed bid auction. We propose GAVEL, a truthful auction mechanism, that is based on the ascending bid auction format, that avoids the pitfalls of existing auction mechanisms that are based on the sealed bid auction format. Using extensive simulations we observe that GAVEL can achieve better performance than existing mechanisms. Second, we study the use of cyclic prefix in Orthogonal Frequency Division Multiplexing. The cyclic prefix does contain useful information in the presence of interference. We discover that while the signal of interest is redundant in the cyclic prefix, the interference component varies significantly. We use this insight to design CPRecycle, an improved OFDM receiver that is capable of using the information in the cyclic prefix to mitigate various types of interference. It improves spectral efficiency by decoding packets in the presence of interference. CPRecycle require changes to the OFDM receiver and can be deployed in most networks today. Finally, home WiFi networks are considered private when encryption is enabled using WPA2. However, experiments conducted in real homes, show that the wireless activity on the home network can be used to infer occupancy and activity states such as sleeping and watching television. With this insight, we propose WiFi Glass, an attack vector that can be used to infer occupancy and activity states (limited to three activity classes), using only the passively sniffed WiFi signal from the home environment. Evaluation with real data shows that in most of the cases, only about 15 minutes of sniffed WiFi signal is required to infer private information, highlighting the need for countermeasures