Reliable Federated Learning for Mobile Networks

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks

A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing with Diverse Sensing Costs

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely cells), Sparse Mobile Crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current Sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in real-world, as the cost of sensing in a subarea can be diverse due to many factors, e.g. condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable bi-objective optimization problem under certain assumptions and approximation. Then, we presented two selection strategies, i.e. Pareto Optimization Selection (POS) and Generalized Cost-Benefit Greedy (GCB-GREEDY) Selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring datasets (Parking, Flow, Traffic, and Humidity) and three cost maps (i.i.d with dynamic cost map, monotonic with dynamic cost map and spatial correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-Query by Committee (QBC) in a sensing cycle. The findings show important implications in Sparse Mobile Crowdsensing for urban context properties

Reputation-aware Trajectory-based Data Mining in the Internet of Things (IoT)

Internet of Things (IoT) is a critically important technology for the acquisition of spatiotemporally dense data in diverse applications, ranging from environmental monitoring to surveillance systems. Such data helps us improve our transportation systems, monitor our air quality and the spread of diseases, respond to natural disasters, and a bevy of other applications. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. While previous research has attempted to improve the quality of the IoT data, they are limited in terms of better realization of the sensing context and resiliency against malicious attackers in real time. For instance, the data fusion techniques, which process the data in batches, cannot be applied to time-critical applications as they take a long time to respond. Furthermore, context-awareness allows us to examine the sensing environment and react to environmental changes. While previous research has considered geographical context, no related contemporary work has studied how a variety of sensor context (e.g., terrain elevation, wind speed, and user movement during sensing) can be used along with spatiotemporal relationships for online data prediction. This dissertation aims at developing online methods for data prediction by fusing spatiotemporal and contextual relationships among the participating resource-constrained mobile IoT devices (e.g. smartphones, smart watches, and fitness tracking devices). To achieve this goal, we first introduce a data prediction mechanism that considers the spatiotemporal and contextual relationship among the sensors. Second, we develop a real-time outlier detection approach stemming from a window-based sub-trajectory clustering method for finding behavioral movement similarity in terms of space, time, direction, and location semantics. We relax the prior assumption of cooperative sensors in the concluding section. Finally, we develop a reputation-aware context-based data fusion mechanism by exploiting inter sensor-category correlations. On one hand, this method is capable of defending against false data injection by differentiating malicious and honest participants based on their reported data in real time. On the other hand, this mechanism yields a lower data prediction error rate

Optimal Order Assignment with Minimum Wage Consideration (OOAMWC)

While the application of crowdsourcing has increased over the years, the technology experiences various issues during implementation. Examples of some of the issues that affect crowdsourcing include task assignment, profit maximizations, as well as time window issues. In some instances addressing some of the issues results in the other issues being overlooked. An example is when assigning tasks to workers, the profits of the workers might not be considered and this ends up affecting the profit maximization aspect. Various algorithms have been proposed to address the task assignment, profit maximizations, and time window issues. However, these algorithms address the issues individually and this results in the occurrence of the other noted issues. Therefore, this calls for the definition of a solution to address the task assignment issue while taking into consideration the time window issue and the minimum wage constraint. Additionally, the solution should address the profit maximization of not only the workers but also the platform and the clients of the platform. To evaluate the efficiency of the proposed solution, a comparison with the different implemented solutions to address individual issues is recommended. Comparing such solutions can provide insight into the efficiency of the proposed approach when addressing multiple issues affecting crowdsourcing