Federated learning (FL) on mobile edge devices suffers from heterogeneous computation and communication capabilities, time-varying wireless channels, and strict battery constraints. Existing client selection methods typically optimize per-round performance while ignoring heterogeneous energy consumption and per-device energy budgets, which may cause premature client dropout and degraded model accuracy. This paper proposes FEPCS, a Fine-grained Energy Planning Client Selection scheme for energy-efficient FL in mobile edge environments. At the client level, FEPCS dynamically adjusts local training workloads based on residual energy and estimates transmission energy using historical channel information. At the system level, it introduces a progressive participation rate and an energy budget deviation metric, and combines these with data contribution into a unified selection criterion. Extensive simulations under heterogeneous settings show that FEPCS achieves higher test accuracy, significantly extends client survival time, and effectively reduces client dropouts compared with existing schemes, while operating under the same energy constraints.

Federated learning (FL) in symbiotic Internet of Things (IoT) networks is a promising collaborative paradigm that utilizes IoT devices to co-train machine learning models, promising to accelerate edge intelligence for 6G. Existing studies on heterogeneous FL in IoT networks focus mainly on the differences in link capacity, ignoring the fundamental impact of channel fluctuation on model transmission and communication energy consumption. FL in symbiotic IoT networks still faces the challenges of heterogeneous and dynamic wireless links and inter-round competition of limited resource allocation, significantly impacting energy efficiency and learning performance. To address this issue, we first model wireless channel fading and dynamics for FL over symbiotic IoT networks and develop a joint optimization model for energy efficiency and learning performance. Then, we propose a novel heterogeneous-aware client sampling scheme to achieve energy-efficient training by exploiting prompt channel state tracking to predict energy consumption and update the deviation of the energy budget of each client promptly to select the optimal set of clients for each training phase. Finally, extensive experiments show that our proposed client sampling scheme significantly outperforms the existing methods and improves energy efficiency by up to$1.6\times $.

In conventional federated learning (FL), multiple edge devices holding local data jointly train a machine learning model by communicating learning updates with a centralized aggregator without exchanging their data samples. Owing to the communication and computation bottleneck at the centralized aggregator and inaccurate learning model caused by the non-independent and identically distributed (IID) data, we here consider a two-tier FL network, in which Internet of Things (IoT) nodes are the core clients that hold data, the model aggregators at the middle tier are the low altitude aerial platforms (UAVs), and the model aggregator at the top-most layer is the high-altitude aerial platform (UAV with relatively high altitude). Under the assumption that each IoT node has parallel computing ability, we study the energy-efficient computation and communication resource allocation in such a network within some time budget. Upon formulating the problem as an optimization problem, we solve the computation and communication resource allocation problems as the separate subproblems within a time frame, and then propose an iterative algorithm to solve the entire problem jointly. More specifically, we solve both the energy-efficient computation and communication resource allocation subproblems using the dual decomposition technique, and then apply a bisection search-based recursive technique to solve the entire energy efficiency problem jointly. Moreover, we propose offline and online client scheduling schemes that not only select the optimal edge nodes for association but also assign workload to each client based on the data quality and workload constraint. With real data, extensive simulations are conducted to verify the effectiveness of the proposed resource allocation scheme. The results further reveal that the learning performance not only is dependent on the computation and communication energy consumption of the FL process but also the model divergence weight owing to the non-IID data at client IoT nodes.

Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.

In this paper, we consider the anti-jamming problem for the uplink scenario in federated learning (FL) networks under the uncertainty condition of jamming channels. We first consider a single-carrier single-user FL network, in which the channel gain of the jammer is known to the FL user in probabilistic terms. We formulate the interaction between the FL user and the jammer by a Bayesian Stackelberg game. Upon the derivation of the best response strategy for both the players, we prove the existence and uniqueness of the equilibrium state. We further extend the game formulation for the multi-user multi-channel case, in which multiple FL users access the global aggregator using multiple channels. Similar to the single-user case, in this game, the jammer acts as the leader and the FL network works as the follower. Since the derivation of the optimal strategy is intractable due to the non-convexity nature of the problem, we provide a suboptimal algorithm that provides an equilibrium state for both the players. Extensive simulations on real data set are conducted to verify the effectiveness of the proposed games for both the single and multi-user cases. The results reveal that the priori probabilities of jamming channels greatly affect the strategies of both the players as well as the equilibrium state.

Wireless sensor network (WSN) enables the continuous monitoring of environmental conditions. These systems are usually powered by batteries. Given that there might be tremendous distributed sensor nodes in a network, battery maintenance must become one of the major challenges for their massive deployment. Energy harvesting technology, by which energy is extracted from the ambient environment, is developed for powering the ubiquitous Internet of Things (IoT) devices using different types of local energy, such as solar, vibration, and wind. In this paper, based on the vibration energy harvesting technology, we introduce a battery-free pavement roughness estimation system (BF-PRES), which provides road roughness information by linking the driving vibration and wireless packet count. Instead of harvesting energy to power an off-the-shelf commercial accelerometer and implementing some algorithms for vibration estimation, BF-PRES simply sends out a BLE Beacon packet, when the accumulated energy arrives at a sufficient level. Given that larger vibration intensity gives higher harvested power, the packet count within a constant time interval should be positively related to the road roughness. Lab testing shows the feasibility of the proposed design. In addition, this study also provides a new design scheme and easy implementation of battery-free or energy-constrained IoT systems.

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.

Being a beneficial service in emergency situations, UAV-aided relay communications have received tremendous attentions in the recent years. In this paper, we consider a three-node UAV-based relay network, in which a mobile UAV establishes communication between two remaining nodes in the system. Despite numerous works available for such a system on the optimization of trajectory and other communication resources, none of these have addressed the delay performance of the system at the granular packet level. Furthermore, it is already established that the equipment of buffer at the relay node provides more flexibility in the delivery of packets through the exploitation of better channel quality. On the other hand, with the continued popularity of multimedia and similar other applications, it is very likely that the source node in the system receives bursty traffic from different external networks. Given that the source and UAV nodes have finite packet-level buffers and the trajectory of the UAV is known, under a bursty traffic model, we aim to study the average end-to-end packet delay and buffer overflow performance of the system. While capturing the predictable channel variation due to the movement of the UAV, we establish a queuing model for the source and UAV nodes based on the stochastic process. Then, from the dynamics of the established queuing model, we derive the average end-to-end packet delay and queue overflow probability of such a system. Through extensive numerical simulation, we justify the accuracy and effectiveness of the proposed analytical model while comparing with three static deployment scenarios of the UAV. Through providing sufficient analytical evidence as well as the numerical results, we exhibit that a mobile relay system outperforms the static relay one in terms of both the delay and buffer overflow metrics.

Recent years have witnessed a steep grow in the multimedia-oriented Internet of Things (IoT) over vehicular networks. Huge volume of multimedia traffic generated from the in-built IoT devices should be delivered among vehicles and immediate surroundings in real time. However, as network nodes with higher mobility, vehicles often experience more unpredictable wireless channels. Such time-frequency diversity poses substantial challenges to achieve pervasive and real-time multimedia connectivity. The hurdle lies in the inability of automatically approaching the subcarrier level channel variations in the existing video codecs. With coarse-grained traffic delivery rate, video decoding fails, and intermittent connection occurs. To break this stalemate, we propose a fine-grained wireless video streaming strategy, namely iCast, that intelligently achieves the most appropriate data rate and frame protection for multimedia traffic in highly mobile vehicular environments. The insight of iCast is a simple joint source-channel rateless code. It reaps the benefits of the frequency diversity to provide fine-grained data rate for the channel in conjunction with suitable protection for the source. Our experiments show that, by harnessing frequency diversity in mobile environments, iCast outperforms the existing competitive wireless video delivery schemes by up to 5 dB peak signal-to-noise ratio.

Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared with other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, the information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared with the downlink ones due to the individual users' power constraints and the discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios.

In this paper, an extended COvariance Matching Estimation Techniques-Extend Invariance Principle (COMET-EXIP) method is proposed to estimate interferometric synthetic aperture radar or interferometric synthetic aperture sonar (InSAS) interferometric phase in the presence of large coregistration errors, even up to one pixel. First, the extended COMET-EXIP method is presented for the application of joint-pixel-model-based interferogram estimation, through choosing a novel “unstructured model” in terms of the parameters to be estimated and decoupling the interesting parameters from the uninteresting “nuisance parameters.” Then, a fast algorithm of COMET-EXIP is proposed for the interferometric phase estimation. Finally, the ambiguity problem of the COMET-EXIP method is solved without introducing performance degradation. The simulated data and real data from the trial InSAS and X-SAR are used to verify the validity of the method. The results show that the method is robust for a wide range of signal-to-noise ratio and has a good performance on both fringe preserving and noise suppressing. In addition, the same computational speed level of the proposed method as that of the pivoting mean filtering is very attractive.