The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users’ daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

The heterogeneous networks which collaborate among Space, Air, Ground, and Sea (SAGS) networks significantly promote the development of the Internet of Things (IoT). Billions of IoT devices in SAGS networks generate massive data to support various applications. We argue that it is crucial to construct a Collaboration Trust Interconnections System (CTIS) to provide the ubiquitous SAGS network accessibility and security. In this paper, a CTIS framework among the Unmanned Aerial Vehicles (UAV), Mobile Vehicles (MVs), and IoT devices is proposed to evaluate trust and select low-cost and high-trust participants to improve data quality. In this framework, MVs record the interaction information to form the verification chains, while the UAV is dispatched to collect baseline data to verify the data reported by MVs, thereby constructing global trust. Then, the hash values of baseline data are delivered to MVs to act as calibration baseline data, which provides a verification certificate for interactions among MVs and constructs local interaction trust. Finally, a greedy-based winner recruitment strategy is proposed to achieve intelligent information control with maximum credibility and cost. The simulation results show that the CTIS framework reduces the cost by 5.62%, reduces the false ratio and packet dropping rate by at least 17.16% and 31.51% compared with previous schemes.

An Intelligent Collaboration Trust Interconnections System for Mobile Information Control in Ubiquitous 5G Networks

A parallel joint optimized relay selection protocol for wake-up radio enabled WSNs

Deep reinforcement learning for computation offloading in mobile edge computing environment

In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) are an impressive technology. These networks are extremely resource constrained and require the design of energy-efficient routing techniques. The clustering and location prediction routing method based on multiple mobile sinks (CLRP-MMSs) for the Mobile Internet of Things (MIoT) is presented in this article. Recently, mobile sinks are used in routing more durability and energy saving in WSN. In this work, first, the entire nodes are divided into clusters, and then each cluster selects a cluster head (CH) by calculating the CH choosing function (CHCF). When clustering runs on networks with moving nodes, the possibility of disconnecting the nodes from CH nodes will cause a lot of data loss. It will change the amount of energy and rate of data received, but the amount of wasted energy is reduced by predicting the location and reducing the sink and CH nodes’ distance. The simulation results using NS-2 clearly showed that the proposed method improves energy consumption at least 28.12% and increases throughput at least 26.74% compared to energy efficient routing algorithm with mobile sink support and high-available and location-predictive data gathering scheme using mobile sink methods.

A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things

Billions of sensors and devices are connecting to the Internet of Thing (IoT) and generating massive data which are benefit for smart network systems. However, low-cost, secure, and efficient data collection from billions of IoT devices in smart city is a huge challenge. Recruiting mobile vehicles (MVs) has been proved to be an effective data collection scheme. However, the previous approaches rarely considered the security. In this paper, a novel Baseline Data based Verifiable Trust Evaluation (BD-VTE) scheme is proposed to guarantee security at a low cost. BD-VTE scheme includes Verifiable Trust Evaluation (VTE) mechanism, Effectiveness-based Incentive (EI) mechanism, and Secondary Path Planning (SPP) strategy, which are respectively used for reliable trust evaluation, reasonable reward, and efficient path adjustment. Among them, an active trust verification mechanism is innovatively proposed in the VTE mechanism, which evaluates the trust of MVs by sending UAVs to perceive IoT devices data as baseline data. This is a fundamental change to the previous passive and unverifiable trust mechanism. The simulation results show that BD-VTE scheme reduces the cost by at least 25.12% ∼ 38.03%, improves the collection rate by 0.91% ∼ 9.65% and increases the accuracy by 10.28% on average compared with the previous strategies.

BD-VTE: A Novel Baseline Data Based Verifiable Trust Evaluation Scheme for Smart Network Systems

In this note, we describe a battery failure detection pipeline backed up by deep learning models. We first introduce a large-scale Electric vehicle (EV) battery dataset including cleaned battery-charging data from hundreds of vehicles. We then formulate battery failure detection as an outlier detection problem, and propose a new algorithm named Dynamic-VAE based on dynamic system and variational autoencoders. We validate the performance of our proposed algorithm against several baselines on our released dataset and demonstrated the effectiveness of Dynamic-VAE.

Shaobo Huang

Papers

BD-VTE: A Novel Baseline Data Based Verifiable Trust Evaluation Scheme for Smart Network Systems

An Intelligent Collaboration Trust Interconnections System for Mobile Information Control in Ubiquitous 5G Networks

Detecting Electric Vehicle Battery Failure via Dynamic-VAE