Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network. When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor. However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed. This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes. The algorithm is divided into four steps. First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing. Second, the entropy method is used to weight the contributions of three indicators. Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model. Finally, using the reliability evaluation model select reliable beacon nodes. Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.

During the end-of-life (EOL) product recovery process, there are a series of combinatorial optimization problems (COPs) that should be efficiently solved. These COPs generally result from reverse logistics (RL) and remanufacturing, such as facility location and vehicle routing in RL, and scheduling, planning, and line balancing in remanufacturing. Each of the COPs in RL and remanufacturing has been reviewed; however, no review comprehensively discusses and summarizes the COPs in both. To fill the gap, a comprehensive review of the COPs in both RL and remanufacturing is given in this paper, in which typical COPs arising at the end of the product life cycle are discussed and analyzed for the first time. To better summarize these COPs, 160 papers published since 1992 are selected and categorized into three modules: facility location and vehicle routing in RL, scheduling in remanufacturing, and disassembly in remanufacturing. Finally, the existing research gaps are identified and some possible directions are described.

Disassembly is the first phase to demanufacture end-of-life (EOL) products that are separated into parts/components for recovery. The quality conditions of EOL products are highly uncertain, which would result in some uncertain information during the disassembly process, e.g., the disassembly time and recovering revenue of each subassembly. It is quite challenging to determine the optimal/near-optimal disassembly solutions under uncertain information. This article studies uncertain disassembly planning (UDP) and proposes a self-adaptive learning approach to quickly identify the near-optimal disassembly solutions. First, we model the UDP by extending Petri nets, where not only disassembly operations but also EOL options of each subassembly are represented in the extended Petri Net. Based on the UDP model, we develop the self-adaptive learning approach, which integrates an approximation procedure for estimating uncertain disassembly information, a Q -learning algorithm for training disassembly samples, and a heuristic method for selecting the best disassembly solution. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-adaptive learning approach. The experimental results demonstrate that our proposed method can efficiently find a better disassembly solution than the existing disassembly solution within 200 trainings in the case study.