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Recent advancements in wireless technology have created an exponential rise in the number of connected devices leading to the internet of things (IoT) revolution. Large amounts of data are captured, processed and transmitted through the network by these embedded devices. Security of the transmitted data is a major area of concern in IoT networks. Numerous encryption algorithms have been proposed in these years to ensure security of transmitted data through the IoT network. Tiny encryption algorithm (TEA) is the most attractive among all, with its lower memory utilization and ease of implementation on both hardware and software scales. But one of the major issues of TEA and its numerous developed versions is the usage of the same key through all rounds of encryption, which yields a reduced security evident from the avalanche effect of the algorithm. Also, the encryption and decryption time for text is high, leading to lower efficiency in IoT networks with embedded devices. This paper proposes a novel tiny symmetric encryption algorithm (NTSA) which provides enhanced security for the transfer of text files through the IoT network by introducing additional key confusions dynamically for each round of encryption. Experiments are carried out to analyze the avalanche effect, encryption and decryption time of NTSA in an IoT network including embedded devices. The results show that the proposed NTSA algorithm is much more secure and efficient compared to state-of-the-art existing encryption algorithms.

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A novel parallelization method of genetic algorithm (GA) solution of the Traveling Salesman Problem (TSP) is presented. The proposed method can considerably accelerate the solution of the equivalent TSP of many complex vehicle routing problems (VRPs) in the cloud implementation of intelligent transportation systems. The solution provides routing information besides all the services required by the autonomous vehicles in vehicular clouds. GA is considered as an important class of evolutionary algorithms that can solve optimization problems in growing intelligent transport systems. But, to meet time criteria in time-constrained problems of intelligent transportation systems like routing and controlling the autonomous vehicles, a highly parallelizable GA is needed. The proposed method parallelizes the GA by designing three concurrent kernels, each of which running some dependent effective operators of GA. It can be straightforwardly adapted to run on many-core and multi-core processors. To best use the valuable resources of such processors in parallel execution of the GA, threads that run any of the triple kernels are synchronized by a low-cost switching mechanism. The proposed method was experimented for parallelizing a GA-based solution of TSP over multi-core and many-core systems. The results confirm the efficiency of the proposed method for parallelizing GAs on many-core as well as on multi-core systems.

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The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data.