International Journal of Computer and Applications

Recently, the development of three-dimensional interference-aware data transmission methods has attracted the attention of scholars due to the increased interest in exploiting and studying the underwater acoustic sensor networks (UASNs). In this paper, an interference aware data transmission protocol based on cellular clustering architecture is proposed. The protocol involves two steps. The first one is an inter-cell time division multiple access (TDMA) scheduling, which reduces acoustic interference by restricting simultaneous data transmission via adjacent routing paths; and the second one is an intra-cell hierarchical routing, which targets efficient data collection in the submarine and reliable data transmission from the seabed to the surface. Moreover, a novel Ekman spiral-based low-cost location prediction method and a void hole recovery scheme are adopted in each step to support the practicability of proposed protocol. Theoretical analysis and simulations indicate that the proposed protocol has advantages related to the quality of service (QoS) of UASNs because the signal interference is significantly mitigated.

Cellular Clustering-Based Interference-Aware Data Transmission Protocol for Underwater Acoustic Sensor Networks

Internet Of Things (IOT) is an inevitable result of the evolution of communication and manufacturing of small low power and effective micro electro mechanical systems (MEMS). Wireless Sensor Network (WSN) is Self organized collected sensors that communicate to each other randomly through waves. Node localization is a major challenge for most of WSN applications due to the difficult environments through which nodes are distributed such as underwater, fires, volcanoes, animal habitat or Battlefields. Moreover, using Global Positioning System (GPS) is costly high and not feasible in indoors. Range based algorithms such as Angle Of Arrival, Time Of Arrival and Received Signal Strength Indicator are alternative for GPS with acceptable accuracy, however they require additional hardware. Range free algorithms such as centroid, approximate point-in-triangulation and distance vector-hop algorithms are economic but less accurate. Lack of accuracy is still an issue in traditional localization algorithms which guide researchers recently to propose optimization techniques with the ability to improve the accuracy of localization based on soft computing algorithms like genetic algorithms, fuzzy logic, neural network and deep learning. The objective of this paper is to give an over view of WSN applications and challenges highlighting the localization problem. The paper also, proposes taxonomy that classifies variant localization algorithms. Moreover, presents a study of different traditional algorithms as well as their improvements based on soft computing techniques. Moreover, the paper summarizes the challenges of localization and represents research points for future work.

Recent achievements in sensor localization algorithms

Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

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Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Renewable energy sources from the perspective of blockchain integration: From theory to application

Digitization and automation have engulfed every scope and sphere of life. Internet of Things (IoT) has been the main enabler of the revolution. There still exist challenges in IoT that need to be addressed such as the limited address space for the increasing number of devices when using IPv4 and IPv6 as well as key security issues such as vulnerable access control mechanisms. Blockchain is a distributed ledger technology that has immense benefits such as enhanced security and traceability. Thus, blockchain can serve as a good foundation for applications based on transaction and interactions. IoT implementations and applications are by definition distributed. This means blockchain can help to solve most of the security vulnerabilities and traceability concerns of IoTs by using blockchain as a ledger that can keep track of how devices interact, in which state they are and how they transact with other IoT devices. IoT applications have been mainly implemented with technologies such as cloud and fog computing, and AI to help address some of its key challenges. The key implementation challenges and technical choices to consider in making a successful blockchain IoT (BIoT) project are clearly outlined in this paper. The security and privacy aspect of BIoT applications are also analyzed, and several relevant solutions to improve the scalability and throughput of such applications are proposed. The paper also reviews integration schemes and monitoring frameworks for BIoT applications. A hybrid blockchain IoT integration architecture that makes use of containerization is proposed.

/pdf/on-blockchain-and-iot-integration-platforms-current-3onq4fbk5a.pdf

On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives

Sensor Network (WSN) localization is an important and fundamental problem that has received a lot of attention from the WSN research community. Determining the absolute and relative coordinate of sensor nodes in the network adds much more meaning to sense data. The research community is very rich in proposals to address this challenge in WSN. This paper, explores the various techniques proposed to address the acquisition of location information in WSN. The paper also evaluate the performance of these techniques based on the energy consumption, the skill and man hours needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. Keywordssensor network, sensor nodes, localization centralized and distributed algorithm, range-based and range- free algorithm.

/pdf/energy-constraints-of-localization-techniques-in-wireless-28izzq9qoa.pdf

Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey

The Internet of Things (IoT) is an intelligent network that connects smart objects to the Internet. A large number of IoT devices are connecting to the Internet, yet many of these devices are insecure, exposing them to a number of security threats. Most IoT devices are resource constrained hence making it difficult to secure them using conventional security techniques. A number of researchers have proposed intrusion detection mechanisms implemented at IoT gateways. IoT devices become vulnerable when the security mechanisms put in place at the gateway fails. This has opened a new research into how conventional security measures can be applied to IoT devices through the development of lightweight and host-based security measures. This paper presents a lightweight and host-based detection and defense mechanism to address DoS attacks on IoT devices. We propose an anomaly DoS detection technique based on heuristics to tackle SYN, ICMP and UDP flood attacks through the application of machine learning. We evaluate the performance of the proposed technique based on CPU and memory utilization efficiency, power consumption, time taken to detect and defend against a DoS attack. The proposed technique achieves promising results with respect to the key performance indicators used; an efficient CPU utilization, an average of 23 megabytes of memory usage, a power consumption of 35.8mW and an average detection and mitigation time of 0.10 and 0.22 seconds respectively.

Lightweight and host-based denial of service (DoS) detection and defense mechanism for resource-constrained IoT devices

The Internet of Things (IoT) is a new paradigm that enables the convergence of smart objects and the internet. This convergence has led to the creation of an intelligent network that connects all things to the internet to exchange information. The direct connection of IoT devices to the internet makes them susceptible to several security threats. Several techniques have been proposed by researchers in enhancing the security of IoT devices. Currently, most IoT products use WiFi as their medium of communication. This makes them prone to conventional WiFi attacks of which one is rogue access points. The lightweight nature of most IoT devices makes it difficult to implement conventional security solutions. In this study, we present a real-time and lightweight algorithm, based on information-theoretic approach, that enables rogue access point detection for embedded IoT devices. This is to ensure that WiFi-enabled IoT devices can intelligently distinguish between legitimate and rogue access points. We evaluate the performance of the proposed algorithm on the CPU utilization efficiency and the time taken in identifying the rogue access point.

Lightweight rogue access point detection algorithm for WiFi-enabled Internet of Things(IoT) devices

The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents.

https://www.mdpi.com/2227-7080/9/3/44/pdf

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Papers

On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives

Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey

Lightweight and host-based denial of service (DoS) detection and defense mechanism for resource-constrained IoT devices

Lightweight rogue access point detection algorithm for WiFi-enabled Internet of Things(IoT) devices

Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention