As an emerging technique, mobile-edge computing (MEC) introduces a new scheme for various distributed communication-computing systems, such as industrial Internet of Things (IoT), vehicular communication, smart city, etc. In this work, we mainly focus on the timeliness of the MEC systems where the freshness of the data and computation tasks is significant. First, we formulate a kind of age-sensitive MEC models and define the average Age-of-Information (AoI) minimization problems of interests. Then, a novel mixed-policy-based multimodal deep reinforcement learning (RL) framework, called heterogeneous multiagent actor–critic (H-MAAC), is proposed as a paradigm for joint collaboration in the investigated MEC systems, where edge devices and center controller learn the interactive strategies through their own observations. To improve the system performance, we develop the corresponding online algorithm by introducing the edge federated learning mode into the multiagent cooperation whose advantages on learning convergence can be guaranteed theoretically. To the best of our knowledge, it is the first joint MEC collaboration algorithm that combines the edge federated mode with the multiagent actor–critic RL. Furthermore, we evaluate the proposed approach and compare it with popular RL-based methods. As a result, the proposed algorithm not only outperforms the baselines on average system age, but also promotes the stability of training process. Besides, the simulation outcomes provide several insights for collaboration designs over MEC systems.

Heavy data load and wide cover range have always been crucial problems for big data processing in internet of things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this paper, we propose a three-layer online data processing network based on MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this paper, we propose an online edge processing scheduling algorithm based on Lyapunov Optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in path planning issue. In this paper, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. As FL does not collect and store the data centrally, it requires frequent model exchange through the wireless network. However, since the aggregation in FL can be partially participated with synchronized frequency, its communication pattern is different from the conventional network. Therein, limited bandwidth and package loss restrict interactions in training. Thus, the network scheduling could largely affect the FL convergence. To figure out the specific effects, we analyze the convergence rate of FL regarding the joint impact of communication and training. Combining it with the network model, we formulate the optimal scheduling problem for FL implementation. The theoretical results could guide the hyper-parameter design in the network and explain the principle of how the wireless communication could influence the FL training process.

In some common scenarios, rare events tend to be more important and worth careful detection. In this case, minor probability event detection rises up as an important issue in big data analytics. With the existence of empirical prior probability distributions of events, one common method to minimize the average detection error is Bayes detection. However, in extreme cases where the prior probability is small, the Bayes method may result in high miss detection rate. To overcome this problem, we proposed a modified detection algorithm by integrating the Bayes method with the message importance measure. Simulation results show that the algorithm significantly reduces the miss detection rate of extremely rare events, which may be helpful to dig rarely occurring phenomenons in big data.

In this paper, we propose soft compression, an lossless compression approach to shape coding for images using location index and codebook of designed shapes with various sizes. This method is different from traditional image compression methods, as it aims at finding the basic shape blocks of pictures to improve the compression ratio from the perspective of information theory and frequency. In particular, the triplets including location and codeword are stored when encoding an image instead of the entire array. It is shown that soft compression can greatly reduce the bandwidth and storage space needed in the process of transmitting and storing the same kind of images.

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.