A Novel Scheme for Controller Selection in Software-Defined Internet-of-Things (SD-IoT).

The software-defined networking (SDN) standard decouples the data and control planes. SDN is used in the Internet of Things (IoT) due to its programmability, central view and deployment of innovative protocols, and is known as SD-IoT. However, in SD-IoT, controller selection has never been studied. Controllers control the network and react to dynamic changes in SD-IoT. As sensors communicate frequently with the controller in SD-IoT, there is a degradation in performance with scalability and an increase in flow requests. Hence, the controller performance and selection are critical for SD-IoT. However, one controller's support for certain functions is high while another's is poor. There are various SD-IoT controllers, and choosing the best one might be a multi-criteria choice. An analytical network decision making process- (ANDP) based technique is employed here to identify feature-based optimal controllers in SD-IoT. The experimental analysis quantifies the high-weight controller from the feature-based comparison. An ANDP-based feature-based controller selection strategy is suggested, which selects the controller with the best feature set first, before comparing performance. This paper's main contribution is to evaluate the ANDP for SD-IoT controller selection based on its features and performance validation in the SD-IoT environment. The simulation results suggest that the proposed controller outperforms the controller selected with previous schemes. Choosing an optimal controller in SD-IoT reduces the delay in both normal and heavy traffic scenarios. The suggested controller also increases throughput while using the central processing unit (CPU) efficiently and reduces the recovery latency in case of failures in the network.

An Effective Approach for Controller Placement in Software-Defined Internet-of-Things (SD-IoT).

The Software-Defined Networking (SDN) paradigm has transferred network intelligence from network devices to a centralized controller. Controllers are distributed in a network to eliminate a single point of failure (SPOF) and improve reliability and balance load. In Software-Defined Internet of Things (SD-IoT), sensors exchange data with a controller on a regular basis. If the controllers are not appropriately located in SD-IoT, the E2E latency between the switches, to which the sensors are connected, and the controller increases. However, examining the placement of controllers in relation to the whole network is not an efficient technique since applying the objective function to the entire network is a difficult operation. As a result, segmenting the network into clusters improves the efficiency with which switches are assigned to the controller. As a result, in this research, we offer an effective clustering strategy for controller placement in SDN that leverages the Analytical Network Process (ANP), a multi-criteria decision-making (MCDM) scheme. The simulation results demonstrated on real Internet topologies suggest that our proposed method outperforms the standard k-means approach in terms of E2E delay, controller-to-controller (C2C) delay, the fair allocation of switches in the network, and the communication overhead.

Adaptive Strategy to Change Firing Phases of Collided Nodes in Extended-Desync TDMA-Based MANETs.

As a multi-hop extension of the desynchronization-based TDMA (Desync-TDMA), the extended Desync-TDMA (Ext-Desync) with self-adapting property is proposed to overcome the limitations of existing CSMA/CA and dynamic TDMA-based schemes for Mobile Ad-hoc Networks (MANETs). However, existing studies overlooked the potential problem of firing message collisions caused by node movements, leading to the severe degradation of MANET networking performance. In this paper, we derive a mathematical model to evaluate the problem due to collisions of firing messages for moving nodes. With the derived model, we propose a method for a collided node to determine whether it changes its firing phase or not, adaptively in a distributed manner, by considering both the collision situation and the slot utilization. The comparative analysis between the proposed method and existing representative ones is also presented for various networking features. The performances of the proposed method are compared with CSMA/CA as well as other existing Ext-Desync-based schemes. The numerical results show that the proposed method achieved much faster resolution and higher slot utilization in collision situations than other Ext-Desync-based schemes. In addition, we also show that the proposed method outperformed the comparable methods, including CSMA/CA, in terms of packet delivery ratios and end-to-end delays.

An Efficient Superframe Structure with Optimal Bandwidth Utilization and Reduced Delay for Internet of Things Based Wireless Sensor Networks.

Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard.

PHND: Pashtu Handwritten Numerals Database and deep learning benchmark.

In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively.

QoS improvement with an optimum controller selection for software-defined networks.

The software-defined networking (SDN) paradigm has simplified the management of computer networks by decoupling data and control planes. Moreover, the separation of the data and control planes has transitioned network complexity from traditional devices to controllers; therefore, controllers have become indispensable entities in SDN. Controllers have multiple features and direct the network from a central point and respond to updates to topological changes. However, the supportive capability of these features is strong in one controller but weak in another. Due to several controllers and each controller having a set of features, selecting an optimal SDN controller can be considered to be a multi-criteria decision-making (MCDM) problem. Herein, a two-step approach is proposed for SDN controller selection. First, the controllers are ranked with analytical network process (ANP) according to their qualitative features which influence the performance of these controllers and then a performance comparison is performed to check for the QoS improvement. The controller with a high-weight value from the feature-based comparison is quantitatively analysed by experimental analysis. The main contribution of this paper is checking the applicability of the ANP for controller selection in SDN considering its features and performance analysis in real-world Internet and Brite topologies. The simulation results show that the controller computed through the proposed approach outperforms the controller selected with existing approaches. The selection of an optimum controller with ANP results in a reduction of topology discovery time and delay in the normal and traffic load scenario. Similarly, an increase in throughput with a reasonable utilization of the central processing unit (CPU) is observed for the proposed controller.