Differing from those traditional vehicle exhaust heat recovery systems which just provided thermal energy directly for cabin warming, integrated Exhaust Energy Recovery (EER) which is researched and developed mainly in recent years aims to convert exhaust thermal energy to mechanical or electric energy for increasing the total thermal efficiency and the total power of powertrain. In the study presented in this paper, an analytic model was built for examining the environmental and economic benefits of integrated EER systems. Then the improvement on the total powertrain efficiency and net reduction of CO 2 emissions were investigated, in terms of the average vehicle used in the UK. Results show that, for light duty vehicles fitted with thermal cycle EER system, the cost increase could be paid back in 10.1 years and CO 2 emission could be paid back in just 1.9 years, compared to Hybrid Electric Vehicle's (HEV's) 11.9 years and 1.4 years for cost and CO 2 emission, respectively. When the annual fuel price increase is considered, the cost pay-back is reduced to 8.1 years for EER vehicles and 8.9 years for HEVs. Higher mileage vehicles will have more obvious advantage for fitting EER system. When doubled annual mileage is considered, EER system can reduce the cost and CO 2 emission pay-back times to 2.7 years and 0.6 years, compared to HEV's 8.5 and 2.7 years, respectively.

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In the environment of the power Internet of Things, equipment network security is mainly analyzed through the physical signal characteristics of the equipment or a single flow characteristic, so as to realize the equipment network abnormality detection. Therefore, a method for detecting network abnormalities of power Internet of Things terminal equipment based on device fingerprints is proposed. Aiming at the problem of incomplete detection methods, a multi-dimensional matching device fingerprint model is established. Firstly, the basic information of the device is collected, such as IP, MAC, etc.; then the network traffic information of the device is analyzed to extract the characteristics of network traffic; then, the frequency of keyword and keyword group of service protocol is counted. The device fingerprint model is established by using the traffic and service characteristics. When the network anomaly occurs, the device fingerprint model can be found effectively. The experimental results show that the device fingerprint model can detect the abnormal behavior of the device.

Due to technique advances, wireless sensor networks (WSNs) are widely used in both military and civil scenarios. A vast amount of data is transmitted to the base station through resource-restrained nodes, which are powered by batteries with limited energy. An energy saving routing protocol is fundamental for network lifetime prolongation. In this paper, we propose TECH (Tree-based Energy-balance Clustering Hierarchy), a scale adaptive hierarchical clustered routing protocol for WSNs. TECH starts the topology construction process from the base station to the far nodes, and then balance the load using the cluster heads within the same level. The in-cluster dynamic data gathering method is used for preventing the cluster heads from dying quickly and reducing the redundant data from transmission. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed protocol. The simulation results show that the proposed protocol outperforms existing protocol in network lifetime prolongation. Furthermore, the protocol can be used in both small and large scale networks, and its performance is stable.

Smart grid has become one hot topic at home and abroad in recent years. Wireless Sensor Networks (WSNs) has applied to lots of fields of smart grid, such as monitoring and controlling. We should ensure that there are enough active sensors to satisfy the service request. But, the sensor nodes have limited battery energy, so, how to reduce energy consumption in WSNs is a key challenging. Based on this problem, we propose a sleeping scheduling model. In this model, firstly, the sensor nodes round robin is used to let as little as possible active nodes while all the targets in the power grid are monitored; Secondly, for removing the redundant active nodes, the sensor nodes round robin is further optimized. Simulation result indicates that this sleep mechanism can save the energy consumption of every sensor node.