Quality of Service (QoS) guarantee is an important issue in building service-oriented applications. Generally, some QoS values of a service are unknown to its users who have never invoked the service before. Fortunately, collaborative filtering (CF)-based methods are proved feasible for missing QoS prediction and have been widely used. However, these methods seldom took the temporal factors into consideration. Indeed, historical QoS values contain more information about user (or service) similarity. Furthermore, as the application environment is dynamic, obtained QoS values usually have short timeliness. Using outdated QoS values will largely decrease the prediction accuracy. In order to resolve this issue, we proposed a time-aware collaborative filtering approach. Firstly, we proposed a QoS model to filter out outdated QoS values, and divided the obtained QoS values into several time slices. Then, we compute the average value of historical QoS as temporal QoS forecast. In addition, by introducing time-aware similarity computation mechanism, we succeed to select real similar neighbor users (or services) and further predict the CF-based QoS based on CF technology. Finally, we can predict the final missing QoS by combining temporal QoS forecast and CF-based QoS prediction. Experiment results show that our approach can receive better prediction precision.

Workflow-based Service-oriented WSNs have recently received a lot of attention from both academia and industry. With well-defined service components, various flexible WSN applications can be developed. However, the key characteristic of WSNs is resource constraints. Sensor nodes in WSNs have limited storage, computation and especially limited energy. As service components in WSNs rely on the data gathered by sensor nodes, they are also resource-constrained. Unfortunately, traditional workflow technologies (i.e., service selection, composition and adaptation) ignore the residual energy of services. This will bring unbalanced energy consumption and furthermore shorten the network lifetime. In order to resolve this issue, we proposed an energy-aware QoS-guaranteed workflow management mechanism. In this mechanism, a new QoS model is first presented to improve the QoS evaluation. Then, based on this QoS model, an efficient service selection schema, which considers both the energy and the QoS of services, is proposed. Furthermore, an adaptation mechanism for balanced energy consumption is also proposed. Experimental evaluations demonstrate the capability of our proposed approach.

Inefficient signal control will not only exaggerate traffic congestion, but also increase the fuel consumption and exhaust emissions. Thus, signal planning is highly important in green transportation. As the Connected vehicle (CV) technology has transformed today’s transportation systems by connecting vehicles and the transportation infrastructure through wireless communication, the CV-based signal control system has seen significant studies recently. Unfortunately, existing signal planning algorithms in use are developed for the signal-intersection, showing low traffic efficiency in the multi-intersection collaborative planning due to ignoring the traffic correlation among the neighboring intersections. In this work, we target the USDOT (U.S. Department of Transportation) sponsored CV-based traffic control system, and implement a multi-intersection traffic network. We model the multi-intersection collaborative signal planning problem as a multi-agent reinforcement learning problem, and present an actor-attention-critic algorithm to improve transportation efficiency and energy efficiency in green transportation, as well as resist congestion attack. Experiment results on the multi-intersection traffic network indicates that 1) compared to the baseline, our approach reduces the total delay by as high as 44.24%; 2) our method transports more vehicles passing the intersections meanwhile reduces the total CO ₂ emissions by 2.40%; 3) under the congestion attack, our approach shows robustness and reduces the total delay by as high as 64.33%.

With the emergence of Service-oriented Architecture (SoA) technologies, service-defined Internet of Things (IoT) applications have been widely developed. However, IoT system is resource-constrained. Devices in IoT usually have limited storage, computation and especially limited energy. Unfortunately, traditional service selection technologies ignore the energy efficiency of services. Although a few studies address energy consumption in service selection or composition, they fail to pay more attention to the dynamic characteristic of IoT, which is not suitable for resource-constrained IoT. In order to resolve this issue, we propose a hierarchical energy efficient service selection approach for resource-constrained IoT. In our approach, a Quality of Service (QoS) guaranteed global constraints decomposition is performed to acquire a group of local QoS constraints. Then, based on the selected local QoS constraints, an energy efficient service selection scheme can be carried out independently according to the dynamic network state. Experimental evaluations demonstrate the capability of our proposed approach.