Hierarchical Federated Learning (HFL) frameworks place edge servers between IoT devices and the cloud server to reduce communication costs and preserve privacy. In practice, however, HFL must handle hierarchical non-IID data across both device and edge levels. At the edge-level, heterogeneity arises because devices connected to the same edge server often share geographic or contextual similarities, giving each server its own optimization goal aligned with its region-specific data distribution rather than with a shared global objective. Existing HFL methods largely ignore this distinction, focusing on training a single global model that can obscure severe underperformance at the edge-level with underrepresented data. Since edge servers often act as operational units, poor performance at an edge implies degraded service quality, undermining system reliability and user trust. We propose Personalized Hierarchical Edge-enabled Federated Learning (PHE-FL), a novel method that produces personalized edge models by adaptively integrating edge- and cloud-level knowledge based on the data distribution of each edge, without incurring additional computational overhead or compromising client privacy. We deploy edge-specific test sets at each edge to ensure its unique data distribution is accurately reflected during evaluation. To the best of our knowledge, this is the first work to explicitly address hierarchical data heterogeneity in a 3-level HFL framework, both in terms of personalization and evaluation. Extensive experiments show that PHE-FL achieves up to 83% higher accuracy than existing edge-accommodated FL methods and maintains robust performance across edge-level non-IIDness, with reduced accuracy fluctuations compared to the state-of-the-art FedAvg with two levels (edge and cloud) aggregation.

Federated Learning (FL) enables privacy-preserving machine learning by allowing clients to collaboratively train models without sharing raw data. Federated Learning as a Service (FLaaS) extends this approach to cloud infrastructures. However, conventional secure aggregation protocols, such as Google's SecAgg and SecAgg+, introduce high computation and communication overheads, particularly in large-scale FLaaS deployments where client dropout rates are limited. To address these challenges, we propose ACCESS-FL, a lightweight, secure aggregation method designed for honest-but-curious FLaaS scenarios with stable network conditions. ACCESS-FL eliminates double masking, Shamir's Secret Sharing, and excessive encryption/decryption by creating shared secrets only between two peers per client, which reduces computation and communication complexity to constant$O(1)$and makes the algorithm independent of network size and comparable to standard FL. ACCESS-FL preserves privacy against inversion attacks and maintains model accuracy equivalent to the FL, SecAgg, and SecAgg+ protocols, proving that reducing overhead does not compromise learning performance and achieves communication and computation costs comparable to standard FL. Experimental evaluations on benchmark datasets (MNIST, FMNIST, and CIFAR-10) demonstrate lower overhead, making ACCESS-FL practical for service-based stable FLaaS applications such as healthcare analytics.

Time-Slotted Channel Hopping (TSCH) is a synchronous medium access mode of the IEEE 802.15.4e standard designed for providing low-latency and highly-reliable end-to-end communication. TSCH constructs a communication schedule by combining frequency channel hopping with Time Division Multiple Access (TDMA). In recent years, IETF designed several standards to define general mechanisms for the implementation of TSCH. However, the problem of updating the TSCH schedule according to the changes of the wireless link quality and node's traffic load left unresolved. In this paper, we use non-cooperative game theory to propose GT-TSCH, a distributed TSCH scheduler designed for low-power IoT applications. By considering selfish behavior of nodes in packet forwarding, GT-TSCH updates the TSCH schedule in a distributed approach with low control overhead by monitoring the queue length, the place of the node in the Directed Acyclic Graph (DAG) topology, the quality of the wireless link, and the data packet generation rate. We prove the existence and uniqueness of Nash equilibrium in our game model and we find the optimal number of TSCH Tx timeslots to update the TSCH slotframe. To examine the performance of our contribution, we implement GT-TSCH on Zolertia Firefly IoT motes and the Contiki-NG Operating System (OS). The evaluation results reveal that GT-TSCH improves performance in terms of throughput and end-to-end delay compared to the state-of-the-art method.

Time-Slotted Channel Hopping (TSCH) is a synchronous Medium Access Control (MAC) technology standardized as a part of IEEE 802.15.4e to provide highly reliable communications for resource-constrained devices. While IETF and IEEE standards defined solutions for the configuration of TSCH nodes, the problem of creating dynamic TSCH schedules has been left open. In this poster, we introduce GT-SF, a distributed TSCH scheduling function that is designed based on the non-cooperative game-theory for low-power Internet of Things (IoT) applications. We implement GT-SF on Zolerita firefly IoT motes and the Contiki-NG operating system to examine its effectiveness. The evaluation results demonstrate that GT-SF outperforms Orchestra (the state-of-the-art method) by enhancing the packet delivery ratio and reducing the latency.

Time-Slotted Channel Hopping (TSCH) was standardized as a part of IEEE 802.15.4e to address the strict reliability and timeliness requirements of low-power Internet of Things (IoT) applications. Setting the size of the TSCH slotframe has a considerable effect on the performance of scheduling algorithms used in IoT networks. Although IETF and IEEE standards define general mechanisms for communication of TSCH nodes, finding the optimal size of the TSCH slotframe has been left open and unresolved. In this poster, we propose an algorithm called S-TSCH to find the optimal size of the TSCH slotframe for maximizing network throughput based on 1) the number of nodes placed in the topology, 2) the data generation rate of applications running on IoT nodes, 3) and the maximum rate of generating TSCH/RPL control packets. To evaluate the performance of our contribution, we implement S-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system. Evaluation results show that our proposed method improves the performance of distributed TSCH scheduling algorithms in terms of reliability and delay.

Time-Slotted Channel Hopping (TSCH) is defined in the IEEE 802.15.4e standard as a share medium access control technology to address reliability and timeliness requirements of low-power Internet of Things (IoT) applications. While standards define mechanisms for the basic configuration and communication of TSCH nodes, the adaptation of the TSCH schedule to traffic dynamics has been left as an open research problem. In this poster, we propose an Optimized Adaptive TSCH Scheduling Function (OA-TSCH) to dynamically adjust the TSCH schedule to the changes in the data traffic loads. We implement OA-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system to evaluate its performance. Evaluation results show that our proposed scheduling function can improve the packet delivery ratio and throughput significantly.

The rapid growth in the number of Internet of Things (IoT) devices has increased the demand for exploring high-throughput communications. Low power IoT networks perform poorly under heavy traffic due to severe congestion and high packet loss problems. Controlling the rate of traffic load is advocated as an effective way to reduce the congestion in traditional networks. However, it poses a major challenge to low power IoT networks due to the lack of infrastructure, dynamic changes of the network topology, and using multi-hop communication through unstable lossy wireless links. To overcome this problem, in this paper we propose an optimized Mobility-Aware Rate Adaptation (MARA) framework based on the game theory. We model the rate control problem as a non-cooperative game where IoT nodes compete for higher bandwidth as selfish players. Based on the Rosen's theorem for concave N-person games, we prove the existence and uniqueness of Nash equilibrium. Finding the optimal solution of the game is modeled as a nonlinear programming (NLP) problem which is solved by using Lagrange multipliers and Karush-Kuhn-Tucker (KKT) optimality conditions. MARA can effectively adapt the transmission rate of each node to the changes in the network topology, traffic dynamics, and energy resources. We implement MARA on Zolerita IoT motes and Contiki operating system to evaluate its performance. Emulation results show that MARA improves the packet delivery ratio by up to 42%, and reduces the end-to-end delay and the energy consumption by up to 32% and 30% respectively.