Efficient Routing Protocol for Location Privacy Preserving in Internet of Things

Internet of Things is a norm which has expanded very swiftly with high magnitude of heterogeneity and functionalities. Security and privacy became the prime factors of Internet of Things due to unsecured character of wireless communication. Thus, because of unsecured network, it is easy for invaders to trace and find the position of nodes during communication and leak the information. Issues related to location information may include sharing of information, storage, sensing, and processing which can be used by external entities in different contexts, i.e. contexts can be: technical, legal, and social. These issues make privacy a major concern. Here, the research this article presents notions of existing privacy models and the amplified techniques using a random path. The article then describes possible solutions to preserve the location of nodes with less transmission time. Results of proposed scheme depict effectual behavior of the approach.

Efficient Data Collection Method in Sensor Networks

Wireless sensor networks are widely used in many fields, such as medical and health care, military monitoring, target tracking, and people’s life, because of their advantages of convenient deployment, low cost, and good concealment. However, due to the low battery capacity of sensor nodes and environmental changes, the energy consumption of nodes is serious and the accuracy of data collection is low. In the data collection method of multiple random paths, due to the uneven geographical distribution between nodes and the influence of the environment, it is easy to cause the communication between nodes to be blocked and the construction of random paths to fail. This paper proposes an efficient data collection algorithm for this problem. The algorithm is improved on the basis of the random node selection algorithm. This method can effectively avoid the failure of random path node selection and improve the node selection of random path in wireless sensor networks. Then, the sensor network in the dynamic environment is analyzed based on the static environment. An efficient data collection algorithm based on the position prediction of extreme learning machines is proposed. This method uses extreme learning machine methods to perform trajectory prediction for nodes in a dynamic environment.

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

Energy conservation, cost, and emission reduction are the research topics of most concern today. The aim of this paper is to reduce the cost and carbon emissions and improve the sustainable development of sheep transportation. Under the typical case of the “farmers–middlemen–slaughterhouses” (FMS) supply model, this paper comprehensively analyzed the factors, sources, and types of cost and carbon emissions in the process of sheep transportation, and a quantitative evaluation model was established. The genetic algorithm (GA) was proposed to search for the optimal path of sheep transportation, and then the model solving algorithm was designed based on the basic GA. The results of path optimization indicated that the optimal solution can be obtained effectively when the range of basic parameters of GA was set reasonably. The optimal solution is the optimal path and the shortest distance under the supply mode of FMS, and the route distance of the optimal path is 245.6 km less than that of random path. From the cost distribution, the fuel power cost of the vehicle, labor cost in transportation, and consumables cost account for a large proportion, while the operation and management cost of the vehicle and depreciation cost of the tires account for a small proportion. The total cost of the optimal path is 26.5% lower than that of the random path, and the total carbon emissions are 36.3% lower than that of random path. Path optimization can thus significantly reduce the cost of different types and significantly reduce the proportion of vehicle fuel power cost and consumables cost, but the degree of cost reduction of different types is different. The result of the optimal path is the key to be explored in this study, and it can be used as the best reference for sheep transportation. The quantitative evaluation model established in this paper can systematically measure the cost and carbon emissions generated in the sheep transportation, which can provide theoretical support for practical application.

Efficient Routing Protocol for Location Privacy Preserving in Internet of Things

Internet of Things is a norm which has expanded very swiftly with high magnitude of heterogeneity and functionalities. Security and privacy became the prime factors of Internet of Things due to unsecured character of wireless communication. Thus, because of unsecured network, it is easy for invaders to trace and find the position of nodes during communication and leak the information. Issues related to location information may include sharing of information, storage, sensing, and processing which can be used by external entities in different contexts, i.e. contexts can be: technical, legal, and social. These issues make privacy a major concern. Here, the research this article presents notions of existing privacy models and the amplified techniques using a random path. The article then describes possible solutions to preserve the location of nodes with less transmission time. Results of proposed scheme depict effectual behavior of the approach.