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The Internet of Things (IoT) is an important part of the new generation of information technology. It utilizes many sensor nodes to collect data and monitor the environment and can be applied in various fields. However, because the energy of sensor nodes is limited and the batteries cannot be replaced, the lifetimes of the sensor nodes are limited by the energy of their batteries. Only a reasonable method for saving energy can reduce the energy loss of the nodes in the communication process. Many studies have found that batteries have the property of battery-friendliness; that is, if they continue working, their energy drops rapidly. However, if they rest after working for a period of time, the batteries' energy can be partially restored. In this paper, an effective relay selection scheme for prolonging the lifetimes of nodes is designed based on the battery-friendly nature of the battery. In the proposed battery-friendly relay selection (BFRS) scheme, the relay node is selected based on the weighted synthesis value of the recovered energy, its distance to the sink and the residual energy. A forwarding node that has recovered more energy and is closer to the sink is more likely to be selected as the relay node because of its larger weight. Hence, the BFRS-based routing scheme can improve the lifetime by recovering more energy. Through extensive experiments, we demonstrate that our BFRS scheme outperforms previously proposed schemes. In a network with 500 nodes, compared with the shortest routing algorithm, the network lifetime is increased by 51.26%, and the amount of transferred data is increased by 42.93%.

Continuous Data Protection (CDP) is an emerging scheme of data backup and recovery for Cyber Security. Compared with traditional data protection techniques, it can provide much higher reliability and allow the data state to be reverted to any point-in-time in the past. CDP continuously captures and logs every disk update, producing large amount of information, thus desperately requires an efficient mechanism of organizing such information (about the disk I/Os) in order to guarantee the performance to be acceptable. However, there are few works which address the organization of history data to improve the recovery efficiency in case of the disaster occurrences. In this paper, three methods of metadata management for Cyber Security are presented, of which two are simple implementations based on MySQL database (named DIR_MySQL and OPT_MySQL respectively) and the other one is designed with consideration of its characteristics. Experimental results show that META_CDP method is far more efficient than the other two and its performance is reasonably acceptable. Furthermore, we discuss in-depth two recovery algorithms, which are full-recovery and incremental recovery. Users can choose the optimal recovery algorithm based on the required recovery time point.

Some control actions (e.g., scheduling overtime production during breaks to catch up throughput) in a serial production line will drive the system to different states at the end time of the actions (defined as the action end-state), and influence the system production in future time. In this paper, we analyze how a selected control action and its end-state will impact system future production. The concept of end-state recovery time and its properties are proposed to describe the pattern of this system future behavior change, and to identify the time it takes to achieve such future production impact. Analytical methods are established to calculate this end-state production impact and recovery time in clean case scenario. In addition, we investigate the end-state production impact in stochastic scenario, and obtain a closed-form solution to evaluate the robustness of the end-state with respect to disruption events. The end-state analysis and evaluation results are critical for assessing and selecting control actions to more quickly and effectively improve system future production, and to deliver robust performance against disruption events.