With recent advancements in artificial intelligence (AI), wireless sensing has recently been accepted as an attractive solution to enable accurate detection of human activities by analyzing the radio signal variations of sensor devices (SDs) using a well-trained AI model. However, due to the limited communication and computation resources at SDs, it is impractical to support energy- and delay-sensitive sensing services by solely processing the massive computation workload at local or offloading it to the edge server (ES) for edge inference. To address this problem, we consider in this paper device-edge co-inference in a wireless sensing system where multiple users collaboratively perform a common inference task. In particular, the AI model deployed at each SD can be split into two sequential parts. Each SD executes the former part of AI model at local, and leaves the remaining part computed at the ES. We aim to minimize the energy consumption of SDs subject to a prescribed inference latency requirement. To this end, we formulate a mixed integer non-linear programming (MINLP) to jointly optimize the model splitting point and system resource allocation, where the major difficulty lies in the tight couplings among splitting decisions of collaborative SDs. To solve the problem, we propose an integrated learning and optimization algorithm named LOP, which tackles the combinatorial model splitting by using a deep reinforcement learning (DRL)-based method, and deals with the remaining resource allocation problem via convex optimization. To gain some engineering insights, we study the optimal model splitting design in a practical wireless indoor crowd counting system, where the optimal splitting point exhibits a threshold-based structure related to the user channel gain. Simulation results demonstrate that the proposed LOP algorithm can achieve a near-optimal energy performance with on average 0.8% optimality gap while enjoying a hundredfold reduction in computation delay.

This paper considers an edge intelligence system where multiple end users (EUs) collaboratively train an artificial intelligence (AI) model under the coordination of an edge server (ES) and the ES in return assists the AI inference task computation of EUs. Aiming at minimizing the energy consumption and execution latency of the EUs, we jointly consider the model training and task inference processes to optimize the local CPU frequency and task splitting ratio (i.e., the portion of task executed at the ES) of each EU, and the system bandwidth allocation. In particular, each task splitting ratio is correlated to a binary decision that represents whether downloading the trained AI model for local task inference. The problem is formulated as a hard mixed integer non-linear programming (MINLP). To tackle the combinatorial binary decisions, we propose a decomposition-oriented method by leveraging the ADMM (alternating direction method of multipliers) technique, whereby the primal MINLP problem is decomposed into multiple parallel sub-problems that can be efficiently handled. The proposed method enjoys linear complexity with the network size and simulation results show that it achieves near-optimal performance (less than 3.18% optimality gap), which significantly outperforms the considered benchmark algorithms.

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computation delay suitable for real-time control under fading channel.

The emerging multi-access edge computing (MEC) technology effectively enhances the wireless streaming performance of 360-degree videos. By connecting a user's head-mounted device (HMD) to a smart MEC platform, the edge server (ES) can efficiently perform adaptive tile-based video streaming to improve the user's viewing experience. Under constrained wireless channel capacity, the ES can predict the user's field of view (FoV) and transmit to the HMD high-resolution video tiles only within the predicted FoV. In practice, the video streaming performance is challenged by the random FoV prediction error and wireless channel fading effects. For this, we propose in this paper a novel two-stage adaptive 360-degree video streaming scheme that maximizes the user's quality of experience (QoE) to attain stable and high-resolution video playback. Specifically, we divide the video file into groups of pictures (GOPs) of fixed playback interval, where each GOP consists of a number of video frames. At the beginning of each GOP (i.e., the inter-GOP stage), the ES predicts the FoV of the next GOP and allocates an encoding bitrate for transmitting (precaching) the video tiles within the predicted FoV. Then, during the real-time video playback of the current GOP (i.e., the intra-GOP stage), the ES observes the user's true FoV of each frame and transmits the missing tiles to compensate for the FoV prediction errors. To maximize the user's QoE under random variations of FoV and wireless channel, we propose a double-agent deep reinforcement learning framework, where the two agents operate in different time scales to decide the bitrates of inter- and intra-GOP stages, respectively. Experiments based on real-world measurements show that the proposed scheme can effectively mitigate FoV prediction errors and maintain stable QoE performance under different scenarios, achieving over 22.1% higher QoE than some representative benchmark methods.