Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment

Self Cite

The proper utilization of road information can improve the performance of relay-node selection methods. However, the existing schemes are only applicable to a specific road structure, and this limits their application in real-world scenarios where mostly more than one road structure exists in the Region of Interest (RoI), even in the communication range of a sender. In this paper, we propose an adaptive relay-node selection (ARNS) method based on the exponential partition to implement message broadcasting in complex scenarios. First, we improved a relay-node selection method in the curved road scenarios through the re-definition of the optimal position considering the distribution of the obstacles. Then, we proposed a criterion of classifying road structures based on their broadcast characteristics. Finally, ARNS is designed to adaptively apply the appropriate relay-node selection method based on the exponential partition in realistic scenarios. Simulation results on a real-world map show that the end-to-end broadcast delay of ARNS is reduced by at least 13.8% compared to the beacon-based relay-node selection method, and at least 14.0% compared to the trinary partitioned black-burst-based broadcast protocol (3P3B)-based relay-node selection method. The broadcast coverage is increased by 3.6–7% in curved road scenarios, with obstacles benefitting from the consideration of the distribution of obstacles. Moreover, ARNS achieves a higher and more stable packet delivery ratio (PDR) than existing methods profiting from the adaptive selection mechanism.

Section: Discussionmentioning

confidence: 99%

ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles

Cao

Jiang

Wang

et al. 2020

Self Cite

“…The simulation results are shown in Figure 4. The reliability was calculated according to Equation (1). The reliability of the baseline node is always 1.…”

Section: Parameter Name Parameter Value (Unit)mentioning

confidence: 99%

“…The promising big data applications based on IoT produced so much data [1,2], and thus it is impractical to transfer all these data to the data center for processing in real time. To address these challenges, the fog computing and edge computing are proposed in recent years as the distributed cloud computing solution for IoT applications.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.Sensors 2020, 20, 122 2 of 20 aggregation site, etc. [5]. On the other side, the fog computing nodes are not necessarily deployed around these access points. To distinguish these two concepts, in this paper, nodes with sufficient communication and computing resources scattered among IoT devices are referred to as fog nodes. The nano-server clusters deployed at the access points are referred to as MEC nodes, which usually have more resources than fog nodes. The collaborative computing of the IoT-end nodes, the fog nodes, the MEC nodes and the data center are referred to as IoT-Fog-Edge computing in this context.The studies on collaboration of Fog, MEC, and Cloud computing usually focus on finding an optimal solution to allocate the IoT tasks to appropriate virtual machines which are hosted on fog, MEC or cloud nodes. In [10], the authors propose to allocate the workload among local MEC servers, neighborhood MEC servers or cloud servers to minimize the energy consumption of MEC nodes subject to delay constraints. Lyapunov drift-plus-penalty-based dynamic queue evaluation is used for the online allocation algorithm. In [11], an optimal algorithm is put forward to determine that the tasks should be allocated to clouds near the end devices or to the one far from the end devices for the energy efficient big data processing. The delay constraints to tasks in near clouds and far cloud are taken into account. In [12], an optimal algorithm for joint task allocation among mobile devices, the computing access point and the remote cloud is proposed, where computing access point can be treated as an MEC node. The studies introduced above are not correlated with the continuous data-fl...

“…However, although mesh signatures may be used in many security fields [3,[7][8][9][10][11][12][13][14][15][16][17][18][19][20], few researchers focused on the improvement of mesh signatures because of their complexity. Currently the generation of mesh signatures consists of two main steps: (1) generating some atomic signatures; (2) generating a final mesh signature based on previous atomic signatures.…”

Section: Introductionmentioning

confidence: 99%

Reusable Mesh Signature Scheme for Protecting Identity Privacy of IoT Devices

Zhang

Lim

et al. 2020

The development of the Internet of Things (IoT) plays a very important role for processing data at the edge of a network. Therefore, it is very important to protect the privacy of IoT devices when these devices process and transfer data. A mesh signature (MS) is a useful cryptographic tool, which makes a signer sign any message anonymously. As a result, the signer can hide his specific identity information to the mesh signature, namely his identifying information (such as personal public key) may be hidden to a list of tuples that consist of public key and message. Therefore, we propose an improved mesh signature scheme for IoT devices in this paper. The IoT devices seen as the signers may sign their publishing data through our proposed mesh signature scheme, and their specific identities can be hidden to a list of possible signers. Additionally, mesh signature consists of some atomic signatures, where the atomic signatures can be reusable. Therefore, for a large amount of data published by the IoT devices, the atomic signatures on the same data can be reusable so as to decrease the number of signatures generated by the IoT devices in our proposed scheme. Compared with the original mesh signature scheme, the proposed scheme has less computational costs on generating final mesh signature and signature verification. Since atomic signatures are reusable, the proposed scheme has more advantages on generating final mesh signature by reconstructing atomic signatures. Furthermore, according to our experiment, when the proposed scheme generates a mesh signature on 10 MB message, the memory consumption is only about 200 KB. Therefore, it is feasible that the proposed scheme is used to protect the identity privacy of IoT devices.