Billions of people around the world send and receive data over online networks daily. Sufficient and redundant data are transmitted over social platforms with AI-assisted in 5G networks. In opportunistic social networks, the main challenge faced by traditional methods is that numerous user nodes participate in data transmission, causing a lot of message copy redundancy and node cache consumption. As a result, the transmission delay of the algorithm is high, the node energy consumption is too large, and even information is lost. To solve these problems, this study establishes an artificial intelligence-based optimization multiple evaluation method. The main purpose of this method is to avoid information loss caused by data loss when reducing data noise, reasonably select communication nodes in opportunistic social network scenarios, optimize data transmission performance, and avoid network congestion. Moreover, our method can effectively identify and exclude potential malicious nodes, reducing the situation that packets are intercepted and discarded. The experiment confirms that the optimized transmission evaluation scheme can effectively reduce routing overheads and energy consumption of a user node, improve the delivery ratio of node data transmission, and ensure the reliability and security of data transmission.

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Facing multidemand tasks and massive heterogeneous resources in an IoT edge computing environment, it is a challenge to obtain reliable and quick response service and allocate application tasks to resource nodes that meet task requirements and user preference. Since IoT edge computing is facing different types of severe attacks, such as message attacks, swing attacks, collusion attack, node attacks, etc., providing a reliable service environment, trust evaluation between edge nodes is necessary. Existing trust computing schemes, however, suffer from a long response period and low malicious detection rate in a dynamic environment. To alleviate these issues, we propose a reliable and efficient task offloading strategy based on the multifeedback trust mechanism (TOSMFTM). First, a reliable and efficient architecture of TOSMFTM is established, which can effectively improve the ability of trust computing and task offloading. Second, according to the broker’s dynamic monitoring of data, a multifeedback trust aggregation model based on time attenuation and interaction frequency is proposed to provide a trusted running environment. Third, a trust weight <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means (TWK-means) clustering algorithm is designed based on resource attributes to enhance the reliability of service, and quickly and accurately cluster out resource nodes required by the task. Finally, we construct a task offloading model based on trust clustering to ensure user experience quality and promote system efficiency. Different from existing task processing models, which only focus on task offloading, our method also carries out resource preprocessing, trust evaluation, and resource clustering before task processing. The experiment verifies the effectiveness and feasibility of our TOSMFTM scheme. 

A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing

Aerial networks have been widely considered a crucial component for ubiquitous coverage in the next-generation mobile networks. In this scenario, mobile edge computing (MEC) and rate splitting multiple access (RSMA) are potential technologies, which are enabled at aerial platforms for computation and communication enhancements, respectively. Motivated from this vision, we proposed a high altitude platform-mounted MEC (HAMEC) system in such an RSMA environment, where aerial users (e.g., unmanned aerial vehicles) can efficiently offload their tasks to the HAMEC for external computing acquisition. To this end, a joint configuration of key parameters in HAMEC and RSMA (referred to as HAMEC-RSMA) such as offloading decision, splitting ratio, transmit power, and decoding order was optimally designed for a processing cost minimization in terms of response latency and energy consumption. Subsequently, the optimization problem was transformed into a reinforcement learning model, which is solvable using the deep deterministic policy gradient (DDPG) method. To improve the training exploration of the algorithm, we employed parameter noises to the DDPG algorithm to enhance training performance. Simulation results demonstrated the efficiency of the HAMEC-RSMA system with superior performances compared to benchmark schemes. 

/pdf/hamec-rsma-enhanced-aerial-computing-systems-with-rate-mjrnzf38.pdf

HAMEC-RSMA: Enhanced Aerial Computing Systems With Rate Splitting Multiple Access

Aerial networks have been widely considered a crucial component for ubiquitous coverage in the next-generation mobile networks. In this scenario, mobile edge computing (MEC) and rate splitting multiple access (RSMA) are potential technologies, which are enabled at aerial platforms for computation and communication enhancements, respectively. Motivated from this vision, we proposed a high altitude platform-mounted MEC (HAMEC) system in such an RSMA environment, where aerial users (e.g., unmanned aerial vehicles) can efficiently offload their tasks to the HAMEC for external computing acquisition. To this end, a joint configuration of key parameters in HAMEC and RSMA (referred to as HAMEC-RSMA) such as offloading decision, splitting ratio, transmit power, and decoding order was optimally designed for a processing cost minimization in terms of response latency and energy consumption. Subsequently, the optimization problem was transformed into a reinforcement learning model, which is solvable using the deep deterministic policy gradient (DDPG) method. To improve the training exploration of the algorithm, we employed parameter noises to the DDPG algorithm to enhance training performance. Simulation results demonstrated the efficiency of the HAMEC-RSMA system with superior performances compared to benchmark schemes.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09775664.pdf

HAMEC-RSMA: Enhanced Aerial Computing Systems with Rate Splitting Multiple Access

Owing to their limited computing power and battery level, wireless users (WUs) can hardly handle compute-intensive workflows by the local processor. Multi-access edge computing (MEC) servers attached to base stations have ample computing power and communication resources, which can be used to address the computation tasks or workloads of WUs. In this study, we design a framework with multiple static and vehicle-assisted MEC servers to handle the workloads offloaded by WUs. For obtaining the optimal computation offloading scheme to minimize the weighted sum cost, including transmission and execution cost, energy consumption cost, and communication bandwidth cost, we model the offloading decision optimization problem as a Markov decision process (MDP). Then, we propose a partial computation offloading scheme based on reinforcement learning (RL) to address the absence of priori knowledge. The proposed scheme can learn the optimal offloading decision based on stochastic workload arrival, the changing channel state, and the dynamic distance between WUs and the edge servers. Moreover, to avoid the curse of dimensionality caused by the complex state and action spaces, we present an improved computation offloading method based on deep RL (DRL) to learn the optimal offloading policy using deep neural networks. Extensive numerical results illustrate that the proposed algorithms based on RL and DRL can autonomously learn the optimal computation offloading policy with no priori knowledge, and their performance are better than that of four baselines algorithms. 

Ma Zhihao

Papers

Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing