Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques

In my dissertation, we focus on the wireless network coexistence problem with advanced physical-layer techniques. For the first part, we study the problem of Wireless Body Area Networks (WBAN)s coexisting with cross-technology interference (CTI). WBANs face the RF cross-technology interference (CTI) from non-protocol-compliant wireless devices. Werst experimentally characterize the adverse effect on BAN caused by the CTI sources. Then we formulate a joint routing and power control (JRPC) problem, which aims at minimizing energy consumption while satisfying node reachability and delay constraints. We reformulate our problem into a mixed integer linear programing problem (MILP) and then derive the optimal results. A practical JRPC protocol is then proposed. For the second part, we study the coexistence of heterogeneous multi-hop networks with wireless MIMO. We propose a new paradigm, called cooperative interference mitigation (CIM), which makes it possible for disparate networks to cooperatively mitigate the interference to/from each other to enhance everyone\u27s performance. We establish two tractable models to characterize the CIM behaviors of both networks by using full IC (FIC) and receiver-side IC (RIC) only. We propose two bi-criteria optimization problems aiming at maximizing both networks\u27 throughput, while cooperatively canceling the interference between them based on our two models. In the third and fourth parts, we study the coexistence problem with MIMO from a different point of view: the incentive of cooperation. We propose a novel two-round game framework, based on which we derive two networks\u27 equilibrium strategies and the corresponding closed-form utilities. We then extend our game-theoretical analysis to a general multi-hop case, specifically the coexistence problem between primary network and multi-hop secondary network in the cognitive radio networks domain. In the final part, we study the benefits brought by reconfigurable antennas (RA). We systematically exploit the pattern diversity and fast reconfigurability of RAs to enhance the throughput of MWNs. Werst propose a novel link-layer model that captures the dynamic relations between antenna pattern, link coverage and interference. Based on our model, a throughput optimization framework is proposed by jointly considering pattern selection and link scheduling, which is formulated as a mixed integer non-linear programming problem

CAPIA: Cloud Assisted Privacy-Preserving Image Annotation

Using public cloud for image storage has become a prevalent trend with the rapidly increasing number of pictures generated by various devices. For example, today\u27s most smartphones and tablets synchronize photo albums with cloud storage platforms. However, as many images contain sensitive information, such as personal identities and financial data, it is concerning to upload images to cloud storage. To eliminate such privacy concerns in cloud storage while keeping decent data management and search features, a spectrum of keywords-based searchable encryption (SE) schemes have been proposed in the past decade. Unfortunately, there is a fundamental gap remains open for their support of images, i.e., appropriate keywords need to be extracted for images before applying SE schemes to them. On one hand, it is obviously impractical for smartphone users to manually annotate their images. On the other hand, although cloud storage services now offer image annotation services, they rely on access to users\u27 unencrypted images. To fulfill this gap and open the first path from SE schemes to images, this paper proposes a cloud assisted privacy-preserving automatic image annotation scheme, namely CAPIA. CAPIA enables cloud storage users to automatically assign keywords to their images by leveraging the power of cloud computing. Meanwhile, CAPIA prevents the cloud from learning the content of images and their keywords. Thorough analysis is carried out to demonstrate the security of CAPIA. A prototype implementation over the well-known IAPR TC-12 dataset further validates the efficiency and accuracy of CAPIA

EDOS: Edge Assisted Offloading System for Mobile Devices

Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users’ devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs

Throughput Optimization in Multi-Hop Wireless Networks with Reconfigurable Antennas

In multi-hop wireless networks (MWNs), interference and connectivity are two key factors that affect end-to-end network throughput. Traditional omni-directional antennas and directional antennas either generate significant interference or provide poor network connectivity. Reconfigurable antenna (RA) is an emerging antenna technology that can agilely switch among many different antenna states including radiation patterns, so as to suppress interference and maintain high connectivity at the same time. In this work, we systematically exploit the pattern diversity and fast reconfigurability of RAs to enhance the throughput of MWNs. We first propose a novel link-layer model that captures the dynamic relations between antenna pattern, link coverage and interference. Based on our model, a throughput optimization framework is proposed by jointly considering pattern selection and link scheduling. Our problem is formulated as a mixed integer non-linear programming problem. The superiority of reconfigurable antennas compared with traditional omni-directional and directional antennas is both theoretically proven, and validated through extensive simulations

Social Norm Cues Classification in Augmented Reality

The growth of AR (Augmented Reality) has remained steady after the technology burst onto the scene in the early 2010s. Privacy in AR is still a fundamental and challenging issue. Immersive AR experiences, such as head mounted displays (HMDs) may be too immersive, distracting the AR user from dynamic objects in the real world which surround them and potentially violating the privacy of innocent bystanders. Another source of rapid technological advancement in the past few years has been the developments of LLMs (Large Language Models) such as OpenAI’s ChatGPT. With extensive training on a vast amount of data, LLMs can assist with a wide range of language-related tasks. Thus, this work aims to develop a system which uses the help of an LLM to detect the attitude of bystanders and subtly communicate this information to AR user in situations where bystanders want them to turn away or shut off their AR system. This proposed framework uses an emotion and gesture recognition tool to identify the subtle social cues of bystanders. The emotion and gesture data is preprocessed and a prompt is generated and delivered to OpenAI’s GPT-3.5 so that the emotions and gestures can be interpreted. Finally, a message is sent to the AR user to inform them about the potential attitude of bystanders. By collecting values for seven different emotions as well as data about head and mouth position in various case studies, we were able to generate convincing responses from GPT-3.5. In addition, we are working towards expanding the framework so that GPT-3.5 may have access to supplemental documents to aid in the interpretation process. This way, future HCI (Human Computer Interaction) studies can be incorporated into our work. Finally, to address the large overhead of using an LLM, we look to create a new LLM to be trained on custom data and run locally

QuickN: Practical and Secure Nearest Neighbor Search on Encrypted Large-Scale Data

In this article, we propose a scheme, named QuickN, which can efficiently and securely enable nearest neighbor search over encrypted data on untrusted clouds. Specifically, we modify the search algorithm of nearest neighbors in tree structures (e.g., R-trees), such that the modified algorithm adapts to lightweight cryptographic primitives (e.g., Order-Preserving Encryption) without affecting the original faster-than-linear search complexity. Moreover, we propose an optimized algorithm on top of our modified search algorithm, where it can significantly save communication overheads of a client without introducing any additional information leakage. We devise an approximate algorithm to k-nearest neighbor search to improve search efficiency by taking a tradeoff in the completeness of search results. In addition, we also demonstrate our design only leaks minimal privacy against advanced inference attacks. Our experimental results on Amazon EC2 show that our algorithms are extremely practical over massive datasets

PDF-DS: Privacy-Preserving Data Filtering for Distributed Data Streams in Cloud

Many real-world applications, from traditional wireless sensor networks to today\u27s Internet of Thing (loT), generate a large amount of data streams distributively. To meet challenges of handling distributed data streams, deploying data streams management systems on public clouds is a prevalent choice. However, as data streams may contain sensitive information, appropriate privacy protection mechanisms must be in place when sending these data to the cloud. Aiming to utilize data outsourced to the cloud without disclosing their privacy, a number of functional encryption schemes have been proposed. Nevertheless, these existing schemes either only consider centralized data source or require pre-defined indexes. This paper proposes a privacy-preserving filtering scheme for distributed data streams outsourced to the public cloud. Our scheme allows cloud servers to filter out corresponding data streams directly over encrypted data. Our scheme enables each data source to encrypt its data independently, and thus the compromise of one data source will not reveal the privacy of others. Thorough analysis and experimental evaluation are carried out to demonstrate the security, effectiveness, and efficiency of our scheme