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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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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.

QoS information is often used as the basic evidence for Web services optimization. In order to collect QoS information of Web services, we propose a lightweight Server-Monitor framework. This proposal contains a WS-Monitor Model related to metamodel of QoS index information and an extending WSDL algorithm that adds QoS collect strategy into WSDL. Experimental results show that Server-Monitor obtains QoS information effectively and causes tiny negative effect on Web service.

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.

With the developing of Wireless Sensor Networks (WSNs), 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.