Journal of High Speed Networks - Volume 26, issue 2

Authors: Hadi, Khaled | Bennaser, Mahmoud

Article Type: Research Article

Abstract: In Wireless Sensor Networks (WSNs), a node location is usually estimated using GPS or network localization algorithms, but this estimation incurs some errors. In this work, we propose techniques to improve node locations and reduce the location errors. Location errors have an impact on location-based routing algorithms. Packets could be delayed in reaching the destination, or dropped due to exceeding the hop count. Altogether, this drains the energy resource of WSNs. Our techniques to reduce the location errors are as follows: First, a node identifies a subset of its neighboring nodes that have estimated Euclidian distances greater than the given …communication range as OutLier (OL) nodes. A node can communicate with OL nodes because their unknown actual locations are within the communication range, but their estimated locations are not. We use, then, mathematical formulas to correct the OL locations to be within the communication range and thus close to their actual locations. This OL method works for a binary sensor model in which the radio signal strength cannot be measured, and the system works on the binary values Received or Not Received. If the sensor model can measure the radio signal strength, then in addition to the OL method, we can use a Received Signal Strength Indicator (RSSI) to calibrate the distance and to reposition the OL nodes. We call this approach OutLiers with Calibration (OLC). Finally, we incorporate our derived mathematical equations into our simulation. The simulation results show that OL and OLC reduce the miss probability of an actual next node, which means better location of the next node. Show more

Keywords: Sensor networks, location errors, geographic routing

DOI: 10.3233/JHS-200626

Citation: Journal of High Speed Networks, vol. 26, no. 2, pp. 89-98, 2020

Authors: Guo, Jing | Zhang, Xu | Wu, Jun

Article Type: Research Article

Abstract: Distributed beamforming is an attractive topic in wireless communication networks. This paper focuses on improving both the convergence speed and the precision level for distributed transmitting beamforming based one bit feedback scheme. A loop learning algorithm is proposed where three states are contained: random perturbation state, reinforcement learning state, optimization decision state. A loop starts with random perturbation state, and then proceeds to the next state according to the information of previous states. Each transmitter adjusts its phase by repeating the loop, so that the carrier phase alignment can be achieved at the receiver when multiple transmitters cooperatively send a …common message signal. The proposed algorithm has advantages over other one bit feedback algorithms for convergence performance. The effectiveness of the algorithm are verified by the provided simulation results. Show more

Keywords: Distributed beamforming, one bit feedback, loop learning, random perturbation

DOI: 10.3233/JHS-200632

Citation: Journal of High Speed Networks, vol. 26, no. 2, pp. 99-109, 2020

Authors: Dou, Zhengxiong | Wang, Xiaonan

Article Type: Research Article

Abstract: This paper focuses on data delivery in IP-based VANET with network gaps and aims to improve the data delivery rate and lower the data delivery latency by bypassing network gaps. In order to achieve these objectives, this paper proposes the routing and handover algorithms to achieve data delivery. Based on the proposed routing and handover algorithms, data can be delivered along the shortest path without network gaps and multiple vehicles can achieve handovers via one handover operation. Finally, this proposal is evaluated, and the data results show that the delivery rate is improved and the data delivery latency is reduced.

Keywords: VANET, data delivery, network gap, routing, handover

DOI: 10.3233/JHS-200633

Citation: Journal of High Speed Networks, vol. 26, no. 2, pp. 111-124, 2020

Authors: Abdulhassan, Aladdin | Ahmadi, Mahmood

Article Type: Research Article

Abstract: Packet classification is a network kernel function that has been widely investigated over the past decade. New networking paradigms, such as software-defined networking and server virtualization, have led to renewed interest in packet classification and its upgrade from classical five-field to many-field classification. With the increasing size of the rule sets and demands for higher throughput, performing many-field packet classification at wire-speed has become challenging. In this paper, we propose an approach to classification by integrating a probabilistic data structure called the Cuckoo filter for approximate membership queries into an R-tree data structure for high-speed, many-field packet classification. Experimental results …show that the proposed classifier obtains high throughput of up to 1.5 M packets per second, and requires little memory to support large rule sets (up to 1 million rules). Show more

Keywords: Software Defined Networking, OpenFlow, many-field packet classification, R-tree, approximate membership query, Cuckoo filter

DOI: 10.3233/JHS-200634

Citation: Journal of High Speed Networks, vol. 26, no. 2, pp. 125-140, 2020

Authors: Majbouri Yazdi, Kasra | Majbouri Yazdi, Adel | Khodayi, Saeid | Hou, Jingyu | Zhou, Wanlei | Saedy, Saeed | Rostami, Mehrdad

Article Type: Research Article

Abstract: One of the most important challenges of social networks is to predict information diffusion paths. Studying and modeling the propagation routes is important in optimizing social network-based platforms. In this paper, a new method is proposed to increase the prediction accuracy of diffusion paths using the integration of the ant colony and densest subgraph algorithms. The proposed method consists of 3 steps; clustering nodes, creating propagation paths based on ant colony algorithm and predicting information diffusion on the created paths. The densest subgraph algorithm creates a subset of maximum independent nodes as clusters from the input graph. It also determines …the centers of clusters. When clusters are identified, the final information diffusion paths are predicted using the ant colony algorithm in the network. After the implementation of the proposed method, 4 real social network datasets were used to evaluate the performance. The evaluation results of all methods showed a better outcome for our method. Show more

Keywords: Diffusion paths prediction, information diffusion patterns, densest subgraphs, ant colony algorithm, centrality

DOI: 10.3233/JHS-200635

Citation: Journal of High Speed Networks, vol. 26, no. 2, pp. 141-153, 2020

Authors: Ahmed, Asif | Saha, Soumitra | Saha, Sudip | Bipul, Md. Younus | Md. Imran, Syad | Muslim, Nasif | Islam, Salekul

Article Type: Research Article

Abstract: The use of the Internet of Things (IoT) is steadily increasing in a wide range of applications. Among these applications, safety and security are some of the prominent applications. Through surveillance systems, we can restrict access to our premises and thus secure our assets. Nowadays face detection and recognition enabled surveillance systems are available in the market, which can detect faces from video frames captured using IP cameras, and then recognize those faces by comparing them with existing databases. However, higher prices and low accuracy are impeding the large scale deployment of those systems. In this paper, we have proposed …a generic architecture for face detection and recognition system from real-time video frames that have been captured through IP cameras and processed using low-cost IoT devices by utilizing Cloud computing services. We have selected two IoT platforms: Eclipse Mosquitto IoT broker and Kaa IoT middleware to implement our proposed architecture. The face detection part is deployed in the IoT devices and the computation-intensive task, i.e., face recognition is carried out in backend Cloud servers. We have executed our experiments in two different Cloud infrastructures: Core Cloud and Edge Cloud and measured the total processing time in different scenarios. The experimental results show that the performance of the Mosquitto broker in terms of total processing time is better than Kaa middleware. Total processing time can be further reduced by deploying a face recognition application from Core Cloud to Edge Cloud. Furthermore, the k-nearest neighbor algorithm shows promising results compared to other face recognition algorithms. Show more

Keywords: IoT platforms, real-time, face recognition, Cloud computing, computation offloading, response time

DOI: 10.3233/JHS-200636

Citation: Journal of High Speed Networks, vol. 26, no. 2, pp. 155-168, 2020