The Internet of things (IoT) presents the future of internet by incorporating objects to communicate with themselves. Different protocols have been emerged to meet the requirements of limited resource objects of IoT. Constrained Application Protocol (CoAP) is the application protocol which is used in IoT communication stack is prone to performance degradation resulted from traffic congestion. Basic congestion control suffers from different shortages and problems which lead to bandwidth consumption, data loss and increased delay. In this paper, a new adaptive congestion control mechanism to enhance performance has been proposed to overcome basic congestion control issues.

Adaptive congestion control mechanism in CoAP Application Protocol for Internet of Things (IoT)

With the rapid development of IoT (Internet of Things), massive data is delivered through trillions of interconnected smart devices. The heterogeneous networks trigger frequently the congestion and influence indirectly the application of IoT. The traditional TCP will highly possible to be reformed supporting the IoT. In this paper, we find the different characteristics of packet loss in hybrid wireless and wired channels, and develop a novel congestion control called NB-TCP (Naive Bayesian) in IoT. NB-TCP constructs a Naive Bayesian distinguisher model, which can capture the packet loss state and effectively classify the packet loss types from the wireless or the wired. More importantly, it cannot cause too much load on the network, but has fast classification speed, high accuracy and stability. Simulation results using NS2 show that NB-TCP achieves up to 0.95 classification accuracy and achieves good throughput, fairness and friendliness in the hybrid network.

https://www.mdpi.com/2079-9292/8/2/134/pdf

Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher

The Internet of Things (IoT) is an emerging communication technology which connects all of the network-enabled devises in all of the world. It is being pervasively utilized in the variety industrial applications such as infrastructure monitoring, utilities, controlling, mission-critical, and security-sensitive applications. Similar to other conventional networks, the IoT applications are also vulnerable to the different kinds of security breaches, threats, and attacks. To mitigate such related problems, various cryptographic, trust-based approaches, and models have been presented in the literature. To address the issue, this paper presents a subjective survey study on IoT trust security and challenges to investigate solutions provided for trust management & handling in the IoT context. The subjective survey is designed based on the commonalities and differences of the presented state-of-the-arts in literature. Then, it classifies the studied state-of-the-art schemes that are based on the trust handling method and summarizes their main contributions and properties. A comparison on the various published papers of these trust-based schemes is tabulated based on the relevant concepts and evaluation metrics derived from literature. Therefore, the paper highlights potentials, existing gaps, and the future research direction in the trust management of the IoT context for further processing and improvement. 

A survey study on trust-based security in Internet of Things: Challenges and issues

A new IP layer multicast congestion control algorithm based on hop to hop Radom Early Detection(RED),which is named STSR,was put forward,and its effectiveness was verified by means of mathematics.It was proved that STSR multicast congestion control had greater sensitivity and multicast transmission link had larger throughput.Simulation by applying NS-2 show that the STSR algorithm might ensure the TCP friendly as well as the distributed control theory of coarser granularity.While raising the IP layer multicast reliability and scalability,STSR guarantees the efficiency and quality to its greatest extent.

Multicast congestion control based on hop to hop limited

Nowadays in the area of Internet of Things (IoT), congestion control has become an essential research area because of people and devices are progressively get connected over the network. The idea behind congestion control mechanisms originated from the point of network bandwidth, node processing ability, server capacities, channel capacity, flow of the link, number and size of distinct flow and channel reliability. Here we have used the concept of different RED, AMID and COAP based congestion control mechanisms. We have measure two level of congestion control that is node level and channel trustability. In this paper we have presented literature review of some of existing congestion control mechanisms. A congestion control model has also been proposed, which uses the measure of node level congestion and channel-trust for decision making.

Congestion Control for IoT Using Channel Trust Based Approach

In the present age of Internet of Things (Iot), congestion control has become an essential research area. The idea behind congestion control originated from the perspective of network bandwidth, node processing ability and server capacities. As more and more people and devices get connected over the net, the need for congestion control increases. In IoT, the monitoring device observes such network features like flow and capacity of a link in network, number and size of distinct flows. In this paper we present literature review of some of existing congestion control mechanisms. A congestion control model has also been proposed, which uses the measure of channel trust for decision making.

A channel trust based approach for congestion control in IoT

Corresponding Author: Surajit Goon Department of Computer Science and Engineering, Brainware University, India Email: goon_surajit@yahoo.co.in Abstract: The two primary restrictions of visual cryptography methods, contract, and security are well-known. Visual cryptography is considered secure if no share reveals information about the secure picture and this image is unreachable when shares are combined. Recursion can be used in a visual cryptography method to ensure the integrity of both arguments. Sometimes, the many shares are generated illogically in such a manner that they sometimes reveal the actual image. The model proposed in the paper has been proved beneficial in reducing the computation, noise, or distortion in the recreated image and helps in providing amended contracts. The suggested scheme uses unique pixel patterns to create a contract for the decrypted image. The contract for the recursion visual cryptography scheme is alterable from the white pixel pattern to new pixel patterns. The elliptic curve cryptography approach is used to ensure the privacy and security of the image. This study has taken a four-share mechanism in which each component is divided into four shares for each color segment. The proposed algorithm uses the Grasshopper algorithm to select the bit's positions where the encryption must be done. Neural Networks have been used as a cross validator in the proposed work model. A new fitness function is designed for the Grasshopper algorithm. The proposed algorithm is compared based on Mean Square Error and Peak Signal to Noise Ratio.

Primary Color Based Numerous Image Creation in Visual Cryptography with the Help of Grasshopper Algorithm, Artificial Neural Network and Elliptic Curve Cryptography

A network-related environment called a “honeyed framework” served to defend official network resources against harm. This framework creates a scenario that motivates the intrusive person to engage in resource-stealing activity. To recognise an unauthorised assault, this framework applied the Attack-detection-procedure. Here, we attempt to identify DoS attacks using the suggested Honeyed framework system. In order to safeguard your network from assaults, NIDS (Network Intrusion Detection System) is one of the first security solutions to make it easier to identify intrusions. In this work, we offer a system that reveals an assault while validating the defense against it. The new cyber security benchmark IoT dataset is used in this white paper to assess the most recent machine learning techniques. This work’s major goal is to develop an architecture that can foresee and stop DDOS attacks, malware, and botnet attacks using these Honeyed designs. Deep Convolution Reinforcement Neural Networks are used for network surveillance and to categories network users from potential threats (DCRNN). A two-step technique of network understanding is used to enhance the functionality of the suggested solution. DSAE (Deep Sparse Auto Encoder) is used for feature engineering challenges at the initial step of the processing process, data pre-processing. The Deep Convolution Reinforcement Neural Network learning strategy is used in the second step to facilitate categorization. The honeyed firewall and web server are then implemented, following the deployment of the honeyed framework. The DCRNN deployment is finished, and users can now be monitored and analyzed as well as data on network users collected. In this study, data from a loT environment was used to test the effectiveness of the published technique. This data included the heterogeneous datasets "IoT-23," "NetML-2020," and "LITNET-2020." With contemporary methods for network discovery, the statistical relevance of this strategy is evaluated.

Debdutta Pal

Papers

A channel trust based approach for congestion control in IoT

Congestion Control for IoT Using Channel Trust Based Approach

Primary Color Based Numerous Image Creation in Visual Cryptography with the Help of Grasshopper Algorithm, Artificial Neural Network and Elliptic Curve Cryptography

Detection of Cyber-attacks using Deep Convolution in Honeyed Framework

Enhanced visual cryptography network (evcn) for secured data transmission combining des and elliptic curve cryptography