Nowadays, mobile edge computing (MEC) appears as a new computing paradigm with its ability to utilize the computing power of both local devices and edge servers. In MEC, edge pricing and resource allocation (EPRA) are two important problems. Edge servers make a profit by selling computing services to users. To maximize their revenue, they need to determine an appropriate price for each user, and decide the amount of resources allocated to each user. However, none of the existing works consider the effect of users’ task assignment strategy on the revenue of the edge. In fact, EPRA will affect the users’ task offloading decision, as they expect to minimize their total cost. In turn, the users’ decision will also influence the revenue of the edge. Therefore, the interaction between mobile users and edge servers should be considered carefully and the interests of both sides need to be maximized simultaneously. In this article, we model the interaction between the two sides as a Stackelberg game. First, given a specified EPRA strategy, we derive a near-optimal task assignment strategy for each user to minimize the total cost based on a greedy algorithm UTA-G. Then, by applying the backward induction method, two pricing and resource allocation schemes with different granularity, i.e., user-granular EPRA and task-granular EPRA are proposed to bring higher revenue to the edge. Experimental results demonstrate that all the proposed algorithms can have good performance in task-intensive, resource-deficient, and workload-heavy scenarios.

The satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. The optimization of system delay and energy consumption is defined as a mixed integer programming problem. Moreover, we present a deep reinforcement learning based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual-network computing offloading decision method for delay and energy consumption based on deep reinforcement learning offloading (DRLO) is proposed, including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, integrating decision-making bias adjustment strategies based on past experience to address the problem of pseudo packet loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10-20%.

With the rapid growth of the number of devices generating and collecting data, dispersion becomes an important feature of data in Internet of Things. Federated learning (FL) provides a feasible way to mine information in such distributed data. It involves training machine learning models over multiple distributed participants without raw data transmission. However, due to the data heterogeneity among participants, the performance of the FL model degrades dramatically. Currently, improved methods mainly reduce data heterogeneity from the perspective of modifying the process of model training, which usually have problems such as high resource consumption or the need for auxiliary data. In this paper, we enhance FL model from another perspective, focusing on data rather than model training. We reduce data heterogeneity by enhancing the trained local data to improve FL performance. Specifically, we propose an FL method based on data augmentation (abbr. FedM-UNE), implementing the classic data augmentation method MixUp in federated scenarios without transferring raw data. Furthermore, in order to adapt this method to regression tasks, we first modify MixUp by bilateral neighborhood expansion (MixUp-BNE), and then propose a federated data augmentation method named FedM-BNE based on it. Compared with the conventional FL method, both FedM-UNE and FedM-BNE increase negligible overhead. To demonstrate the effectiveness, we conduct exhaustive experiments on six datasets employing a variety of loss functions. The results indicate that FedM-UNE and FedM-BNE consistently improve the performance of the FL model. Moreover, our methods are compatible with existing FL enhancements, which yield further improvements in performance.

With the rapid development of the Internet of Things (IoTs), wireless sensor networks (WSNs) and related techniques, the amount of sensory data manifests an explosive growth. In some applications of IoTs and WSNs, the size of sensory data has already exceeded several petabytes annually, which brings too many troubles and challenges for the data collection, which is a primary operation in IoTs and WSNs. Since the exact data collection is not affordable for many WSN and IoT systems due to the limitations on bandwidth and energy, many approximate data collection algorithms have been proposed in the last decade. This survey reviews the state of the art of approximatedatacollectionalgorithms. Weclassifythemintothreecategories: themodel-basedones, the compressive sensing based ones, and the query-driven ones. For each category of algorithms, the advantages and disadvantages are elaborated, some challenges and unsolved problems are pointed out, and the research prospects are forecasted.

Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices. It inherently assumes a uniform capacity among participants. However, due to different conditions such as differing energy budgets or executing parallel unrelated tasks, participants have diverse computational resources in practice. Participants with insufficient computation budgets must plan for the use of restricted computational resources appropriately; otherwise, they would be unable to complete the entire training procedure, resulting in model performance decline. To address this issue, we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose computationally customized federated averaging (CC-FedAvg), which allows participants to determine whether to perform traditional local training or model estimation in each round based on their current computational budgets. Both theoretical analysis and exhaustive experiments indicate that CC-FedAvg has the same convergence rate and comparable performance as FedAvg without resource constraints. Furthermore, CC-FedAvg can be viewed as a computation-efficient version of FedAvg that retains model performance while considerably lowering computation overhead.

Multicasting is a fundamental network service for one-to-many communications in wireless sensor networks. However, when the sensor nodes work in an asynchronous duty-cycled way, the sender may need to transmit the same message several times to one group of its neighboring nodes, which complicates the minimum energy multicasting problem. Thus, in this paper, we study the problem of minimum energy multicasting with adjusted power (the MEMAP problem) in the duty-cycled sensor networks, and we prove it to be NP-hard. To solve such a problem, the concept of an auxiliary graph is proposed to integrate the scheduling problem of the transmitting power and transmitting time slot and the constructing problem of the minimum multicast tree in MEMAP, and a greedy algorithm is proposed to construct such a graph. Based on the proposed auxiliary graph, an approximate scheduling and constructing algorithm with an approximation ratio of 4 l n K is proposed, where K is the number of destination nodes. Finally, the theoretical analysis and experimental results verify the efficiency of the proposed algorithm in terms of the energy cost and transmission redundancy.