The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes.

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Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

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

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

Security and privacy protection in cloud computing: Discussions and challenges

Intelligent resource allocation management for vehicles network: An A3C learning approach

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

A Novel Load Balancing and Low Response Delay Framework for Edge-Cloud Network Based on SDN

In recent years, cloud computing becomes more and more popular. Users outsource large amount of encrypted documents to the cloud in order to avoid information leakage. Searchable encryption technique is a desirable service to enable users search on encrypted data. In most existing searchable encryption schemes, they only provide exact keyword search. Fuzzy keyword search improves the system usability because it allows users to make spelling errors or format inconsistencies. Besides, verifiable encryption schemes usually consider a semitrusted server and verify the authenticity of the search results. However, the server may be malicious, which may modify/delete some encrypted files or forge erroneous results in order to save its storage space or computation ability. In this paper, we investigate the searchable encryption problem in the presence of a malicious server, the verifiable searchability is needed to provide users the ability to detect the potential misbehavior. We propose a verifiable and dynamic fuzzy keyword search (VDFS) scheme to offer secure fuzzy keyword search, update the outsourced document collection and verify the authenticity of the search result. Our scheme is proved universally composable (UC) security by rigorous security analysis.

A Novel Verifiable and Dynamic Fuzzy Keyword Search Scheme over Encrypted Data in Cloud Computing

With the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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Computation offloading through mobile vehicles in IoT-edge-cloud network

With increasing technological facilities, billions of sensor devices are deployed in the smart city, which allows to sense and collect the status and information of all kinds of infrastructures in the city. Those large number of sensing nodes form many self-organized wireless sensor networks (WSNs). Although the single WSN has been widely studied by researchers. However, few studies have focused on how to effectively connect to Internet of Things (IoT) and collect data in many decentralized WSNs of smart city with a low cost. In this paper, a cluster head rotation joint mobile vehicle data collection (CHR) scheme is proposed to effectively collect data from many decentralized WSNs in smart city with a low cost. In CHR scheme, each WSN selects one or multiple nodes which can connect to mobile vehicles as the cluster heads. Then all the in-network sensor nodes send their data to cluster head through multi-hop communication, due to the cluster head can connect to mobile vehicles, so when mobile vehicles pass by cluster head, the data of cluster head can be sent to mobile vehicles and brought to the data center to process. We first propose a single cluster head rotation joint mobile vehicle data collection (SCHR) scheme to collect data by only using a single CH in a WSN in which the CH rotation and clustering algorithms are carefully designed to balance in-network energy consumption. Then multiple cluster head rotation joint mobile vehicle data collection (MCHR) scheme is proposed to further balance the energy consumption and prolong the network lifetime in which multiple cluster heads are selected to be jointly responsible for the data collection task. The extensive experiments show that the CHR scheme has good performance in the network energy, network lifetime and network transmit capacity.

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Xiaoyu Zhu

Papers

Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

A Novel Load Balancing and Low Response Delay Framework for Edge-Cloud Network Based on SDN

A Novel Verifiable and Dynamic Fuzzy Keyword Search Scheme over Encrypted Data in Cloud Computing

Computation offloading through mobile vehicles in IoT-edge-cloud network

Data Collection Through Mobile Vehicles in Edge Network of Smart City