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.

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the Internet of Things (IoT). The main aims of the autonomous things, and one of advanced of IoT, is that it creates a flexible environment that enables movement and communication between objects anytime, anywhere, by saving computing power and utilizing efficient wireless communication capability. However, the existing LEACH method is only based on the model with a static topology, but a case for a disposable sensor is included in an autonomous thing’s environment. With the increase of interest in disposable sensors which constantly change their locations during the operation, dynamic topology changes should be considered in LEACH. This study suggests the probing model for randomly moving nodes, implementing a change in the position of a node depending on the environment, such as strong winds. In addition, as a method to quickly adapt to the change in node location and construct a new topology, we propose Q-learning LEACH based on Q-table reinforcement learning and Fuzzy-LEACH based on Fuzzifier method. Then, we compared the results of the dynamic and static topology model with existing LEACH on the aspects of energy loss, number of alive nodes, and throughput. By comparison, all types of LEACH showed sensitivity results on the dynamic location of each node, while Q-LEACH shows best performance of all.

Geographic information-based Data Transmission and Cooperative Communication for Vehicular Networks

With the advancements in Vehicular communication technologies in automobile engineering leads to enhancement of modern societies by utilizing Internet based data communication in a vehicular network to effectively avoid accidents and traffic congestions using Multi Input Multi Output (MIMO) cooperative relay technique for enhancing the aspects of performance by reduction of transmission energy consumption by taking the advantage of spatial and temporal diversity gain in a vehicular network as the conventional routing based on topology is merely not suitable over a dynamic vehicular network environment as GPS is used to identify effective route [4].In this paper we propose applications of cooperative communication techniques and their survey for identifying close relationship between forwarding and addressing techniques in a vehicular network and further we compare performance and energy consumption of cooperative techniques with the traditional multi-hop technique over Rayleigh channel using MQAM for optimization.

Geographic information-based Data Transmission and Cooperative Communication for Vehicular Networks

With the advancements in Vehicular communication technologies in automobile engineering leads to enhancement of modern societies by utilizing Internet based data communication in a vehicular network to effectively avoid accidents and traffic congestions using Multi Input Multi Output (MIMO) cooperative relay technique for enhancing the aspects of performance by reduction of transmission energy consumption by taking the advantage of spatial and temporal diversity gain in a vehicular network as the conventional routing based on topology is merely not suitable over a dynamic vehicular network environment as GPS is used to identify effective route [4].In this paper we propose applications of cooperative communication techniques and their survey for identifying close relationship between forwarding and addressing techniques in a vehicular network and further we compare performance and energy consumption of cooperative techniques with the traditional multi-hop technique over Rayleigh channel using MQAM for optimization.

Energy-Efficient UAV-Enabled MEC System: Bits Allocation Optimization and Trajectory Design

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.

Identifying Faulty Nodes in Wireless Sensor Network to Enhance Reliability

WSN having several major issues to deliver information from source to sink. Such that secure data transmission, energy consumption, balancing the load and fault tolerance will be the major tasks. In the WSN, one node communicates with its neighbour nodes with limited energy source. So if any node does not work properly or become faulty nodes, which have less initial energy level, bandwidth to communicate with faulty nodes. Hence there is a need of system which can overcome with fault and give the reliable communication. Fault may also occur due to the dislocation of sensor nodes, battery depletion or instability of transmission medium.This proposed work will identify the faulty nodes and calculate the end to end delay as well as residual energy. Hence a reliable communication for all nodes in the WSN can be attained.

Energy-Effective Data Gathering for UAV-Aided Wireless Sensor Networks

Unmanned aerial vehicles (UAVs) are capable of serving as a data collector for wireless sensor networks (WSNs). In this paper, we investigate an energy-effective data gathering approach in UAV-aided WSNs, where each sensor node (SN) dynamically chooses the transmission modes, i.e., (1) waiting, (2) conventional sink node transmission, (3) uploading to UAV, to transmit sensory data within a given time. By jointly considering the SN’s transmission policy and UAV trajectory optimization, we aim to minimize the transmission energy consumption of the SNs and ensure all sensory data completed collected within the given time. We take a two-step iterative approach and decouple the SN’s transmission design and UAV trajectory optimization process. First, we design the optimal SNs transmission mode policy with preplanned UAV trajectory. A dynamic programming (DP) algorithm is proposed to obtain the optimal transmission policy. Then, with the fixed transmission policy, we optimize the UAV’s trajectory from the preplanned trace with recursive random search (RRS) algorithm. Numerical results show that the proposed scheme achieves significant energy savings gain over the benchmark schemes.

Novel Energy-Efficient Data Gathering Scheme Exploiting Spatial-Temporal Correlation for Wireless Sensor Networks

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.

An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks

In energy-constrained wireless sensor networks (WSNs), the design of an energy-efficient smart strategy is a key to extend the network lifetime, but the unbalance of energy consumption and node load severely restrict the long-term operation of the network. To address these issues, a novel routing algorithm which considers both energy saving and load balancing is proposed in this paper. First of all, the transmission energy consumption, node residual energy and path hops are considered to create the link cost, and then a minimum routing graph is generated based on the link cost. Finally, in order to ensure the balance of traffic and residual energy of each node in the network, an “edge-cutting” strategy is proposed to optimize the minimum routing graph and turn it into a minimum routing tree. The simulation results show that, the proposed algorithm not only can balance the network load and prolong the lifetime of network, but meet the needs of delay and packet loss rate.