In this paper, we propose an adaptive mechanism of trap coverage with a robust area coverage model, which employs mobile sensors for applications in mobile sensor networks (MSNs) and in the Internet of Things (IoT). Many promising applications including the target tracking and mobile sensing can be reasonably realized and improved after incorporating the characteristics of trap coverage mechanism on the basis of adaptively adjusting the trap size and sensor mobility. The trap evidently exists throughout the deployment of sensors in wireless sensor networks, in which the target has predictably vanished or application service remains undetected. The properties of the trap in the trap coverage mechanism are contrary to the purpose of target tracking and services detection. This creates a serious problem for target tracking and services detection in MSNs. This paper proposes a robust mechanism of trap coverage involving the use of mobile sensors in target tracking and services detection for applications in MSNs. The experimental results revealed that the proposed method efficiently reduces the target-missing time and the total number of unavailable sensors, and also enhances the maintenance of trap coverage through the movement of mobile sensors in MSNs based on the simulation results and analysis. Performance comparison is conducted by adjusting the total amount of sensors in an adaptively trap coverage. The novel mechanism makes MSNs a much more flexible and provides a cost-effective solution in IoT than static sensor networks.

In this paper, a new repetition finder to be used with dynamic Huffman (1952) coding is proposed to improve the compression efficiency by reducing the redundancy due to string repetitions. Compared to the repetition finder proposed by Yokoo (1991), the proposed scheme effectively increases the numbers of consecutive symbols in the repetition mode and the total number of symbols in the repetition mode. Experimental results show that the proposed method outperforms the repetition finder of Yokoo by 14-40% in compression ratios with about the same memory requirement and running time.

The problems arising in the modeling and coding of digital Chinese character patterns for noiseless compression purposes are discussed. The modeling is intended to capture the maximum redundancies of the source under the consideration of relevant parameters. In this study, a redundancy-gathering algorithm is proposed in which a 2-D parsing tree is used, and the nodes to extend the tree are ranked according to the maximum redundancy gathered. Hence, the modeling represented by the tree can achieve great performance in the small number of nodes. The algorithm is then applied to evaluate the performance of arithmetic coding for digital Chinese character patterns. The results are compared to those of the traditional compression methods. In the same complexity, the authors find that their algorithm can improve the coding efficiency as high as 25.54%, 30.23%, 32.17%, 15.90%, and 27.58%, for five Chinese fonts, respectively.