Security enhanced optimal trajectoryselection for mobile sink using reinforcement learning

Wireless visual sensor networks (WVSNs) have emerged as a strategic inter disciplinary category of WSN with its visual sensor based intelligence that has garnered considerable attention. The growing demand for energy efficient and maximized life time networks in highly critical applications like surveillance, military and medicine has opened up more prospects as well as challenges in the deployment of WVSNs. Multi-hop communication in WVSN results in overloading of intermediate sensor nodes due to its dual function in the network which results in hotspot effect. This can be mitigated with the help of mobile sinks and rendezvous points based route design. But mobile sinks has to visit every cluster head to gather data which results in longer traversal path and higher latency and power consumption related issues if not addressed properly will impact the performance of the network. Our objective is to analyze and determine the optimal trajectory for mobile sink node traversal with the help of a high quality transmission architecture integrated with reinforcement learning and isolation forest based anomaly detection to propose an energy efficient meta-heuristic approach to enhance the performance of network by reducing the latency and securing the network against possible attacks.

Research on Tourism Route Planning Based on Artificial Intelligence Technology

Study on designing reasonable travel routes with the least time cost and the highest experience index was conducted. An artificial intelligence-based wireless sensor travel route planning study is proposed. First, the improved TSP route planning model is built at the least time consumption and combines the normal distributed random number (ND) with the genetic algorithm (GA) and proposes the ND-GA algorithm, analyzes the overall structure, node structure, communication mode, and network coverage of the wireless sensor network, and gives a mathematical model of wireless transmission energy consumption. Using the proposed algorithm to solve the travel route and detailed itinerary, with time, the 10-year travel route design model based on multitarget dynamic optimization finally detailed analysis of the model results and sensitivity analysis results showing that the application of AI wireless sensor technology can also make the scenic work more efficient; for example, a face recognition system can improve the speed of ticket checking. Although the application of AI technology is widely used in tourism activities, there are some problems, which require the continuous optimization and innovation of AI wireless sensor technology by relevant practitioners, so that it can better serve tourists.

Using Neural Networks for Bicycle Route Planning

This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.

Machine Learning-Based Optimization for Subsea Pipeline Route Design

Optimal route selection for the subsea pipeline is a critical task for the pipeline design process, and the route selected can significantly affect the overall project cost. Therefore, it is necessary to design the routes to be economical and safe. On-bottom stability (OBS) and fixed obstacles like existing crossings and free spans are the main factors that affect the route selection. This article proposes a novel hybrid optimization method based on a typical Machine Learning algorithm for designing an optimal pipeline route. The proposed optimal route design is compared with one of the popular multi-objective optimization method named Genetic Algorithm (GA). The proposed pipeline route selection method uses a Reinforcement Learning (RL) algorithm, a particular type of machine learning method to train a pipeline system that would optimize the route selection of subsea pipelines. The route optimization tool evaluates each possible route by incorporating Onbottom stability criteria based on DNVGL-ST-109 standard and other constraints such as the minimum pipeline route length, static obstacles, pipeline crossings, and free-span section length. The cost function in the optimization method simultaneously handles the minimization of length and cost of mitigating procedures. Genetic Algorithm, a well established optimization method, has been used as a reference to compare the optimal route with the result from the proposed Reinforcement Learning based optimization method. Three different case studies are performed for finding the optimal route selection using the Reinforcement Learning (RL) approach considering the OBS criteria into its cost function and compared with the Genetic Algorithm (GA). The RL method saves upto 20% pipeline length for a complex problem with 15 crossings and 31 free spans. The RL optimization method provides the optimal routes, considering different aspects of the design and the costs associated with the various factors to stabilize a pipeline (mattress, trenching, burying, concrete coating, or even employing a more massive pipe with additional steel wall thickness). OBS criteria significantly influence the best route, indicating that the tool can reduce the pipeline's design time and minimize installation and operational costs of the pipeline. Conventionally the pipeline route optimization is performed by a manual process where the minimum roule length and static obstacles are considered to find an optimum route. The engineering is then performed to fulfill the criteria of this route, and this approach may not lead to an optimized engineering cost. The proposed Reinforced Learning method for route optimization is a mixed type, faster, and cost-efficient approach. It significantly minimizes the pipeline's installation and operational costs up to 20% of the conventional route selection process.