Optimization of The Energy Efficiency in Smart Internet of Vehicles Assisted By MEC

Smart Internet of Vehicles (IoV) as a promising application in Internet of Things (IoT) emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes (eVNs) upload and download data through an anchor node (AN) which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer (SWIPT) technology so as to compensate the battery limitation of eletric vehicles. Moreover, the anchor node equips with full-duplex (FD) and multi-input and multi-output (MIMO) technologies for futher improve the spectrum efficiency. Taking into account the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. In order to solve this nonconvex problem, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Numerical simulations demonstrate the effectiveness of the proposed scheme comparing with the benchmark schemes.

An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT

In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.

A distributed localization method for mobile nodes

In Wireless Sensor Networks(WSNs), the location services are the basis of many application scenarios. However, for the range-based localization method, the localization accuracy and the system robustness of the distributed localization system are difficult to guarantee, due to the uncertainty of the distance estimation and position calculation are affected by the node state and communication uncertainty. In this paper, we propose the distributed localization method based on anchor node selection and Particle Filter optimization. In this method, we analyze the uncertainty of error propagation in the Least-squares method and find that there is a proportional relation between localization error and uncertainty propagation. According to this relationship, we propose the corresponding optimization criterion methods of anchor nodes. To optimize the initial localization results, we present the distributed localization method based on anchor node optimal selection and Particle Filter. Simulation results show that the methods we proposed could effectively improve the localization accuracy of the mobile nodes and the robustness of the system.

TOWARDS REPRESENTING TRANSITIONAL SPACES: DEVELOPMENTAL DIRECTION FOR INDOORGML ANCHOR NODE

Naturally, human beings freely navigate indoor space to outdoor space and optionally to another indoor space. However, currently available data models to represent space do not fully reflect this freedom and continuity of movement. These shortfalls hinder the development of location-based applications from aiding this navigation activity and affect the accuracy and optimality of route analysis. Existing models used for this purpose either represent indoor and outdoor space separately or use direct links that do not fully represent the freedom of movement and the complexity of urban areas. While these approaches use single-feature representations of the connection of these spaces through nodes for the building entrances, Transitional Spaces exist at these locations and must be represented accordingly in navigation networks. In this paper, we illustrate how currently defined IndoorGML concepts can be utilized for integrating indoor and outdoor navigation networks through the Transitional Spaces. We perform an experimental case using sample data to demonstrate the limitations of this model. From this, we discuss the developmental direction of the Anchor Node concept towards developing a model to fully represent navigation on an integrated indoor-outdoor network.

Three-Dimensional UWSN Positioning Algorithm Based on Modified RSSI Values

Sensor nodes in underwater wireless sensor networks (UWSNs) are in a three-dimensional space, and water fluidity continuously changes the positioning in water, the clock synchronization of underwater nodes is challenging, and ranging algorithms affected by water flow produce large errors. A three-dimensional UWSN positioning algorithm based on modified RSSI values is proposed to address the problem of UWSN positioning algorithms being susceptible to water influence and prone to unstable positioning and large positioning errors. An unlocated node screens the received anchor node signal strength and then makes a weighted correction to reduce the influence of the water environment and improve the ranging accuracy. A position estimation model is proposed and combined with a three-dimensional underwater model and least squares method to deduce the unlocated node’s position on the basis of the distance between the unlocated node and the anchor node. The proposed algorithm effectively reduces the influence of the water environment on the ranging algorithm’s accuracy and improves the performance of three-dimensional underwater positioning algorithms. Simulation results show that the proposed algorithm can effectively reduce the influence of the underwater environment on positioning algorithms.

A STUDY OF FRACTAL GEOMETRY IN WIRELESS SENSOR NETWORKS

Fractal geometry is a subject that studies non-integer dimensional figures. Most of the fractal geometry figures have a nested or recursive structure. This paper attempts to apply the nested or recursive structure characteristics of fractal geometry to wireless sensor networks. We selected two filling curves, Node-Gosper and Moore, as our research subjects. Node-Gosper Curve is a curve based on node-replacement with a fractal dimension of two. Its first-order graph consists of seven basic line segments. When the hierarchy becomes larger, it can be filled with a hexagonal-like shape. To allow the mobile anchor node of wireless sensor networks to walk along this curve, the number of levels of the Node-Gosper Curve can be adjusted according to parameters such as the sensing area and transmission range. Many space-filling curves have the common shortcoming that they cannot loop on their own, that is, the starting point and the end point are not close, which will cause the mobile anchor node to use extra paths from the end point back to the starting point. The Moore curve has a self-loop, i.e., the starting point and the ending point are almost at the same position. This paper applies Moore curve to the path planning of the mobile anchor node. We can use this path to traverse the entire sensing area and stay in the central point of each square cluster to collect the information of the nodes where the events occurred. The self-loop characteristic of the Moore curve is expected to reach each sensor to collect data faster than other space filling curves, that is, the transmission latency of the sensor traversal will be reduced