The Underwater Wireless Acoustic Sensors are facing several challenges due to their limited energy power that can significantly affect their network performances, hence the design of an efficient and reliable routing protocol for the underwater sensor communication is becoming the main purpose for the researchers, however, in this article, a new mechanism is proposed to balance the underwater network energy consumption due to the frequent data packet forwarding, whereas the protocol uses a method that is based on the historical nodes energy, where sensor that are excessively consuming energy are considered as overloaded node and has to be excluded from the forwarding process depending on their priority value. The implementation and simulation have been performed using NS-2 network simulator, based on the well-known protocol for the underwater acoustic communication ‘Vector-Based Forwarding routing protocol’, the results shows the performance of the proposed mechanism over the VBF in term of energy consumption and efficiency, successful delivery data, and end to end delay.
Low power wide area networks (LPWAN) technologies offer reasonably priced connectivity to a large number of low-power devices spread over great geographical ranges. Long range (LoRa) is a LPWAN technology that empowers energy-efficient communication. In LoRaWAN networks, collisions are strongly correlated with spreading factor (SF) assignment of end-nodes which affects network performance. In this work, SF assignment using machine learning models in simulation environment is presented. This work examines three approaches for the selection of the SF during LoRa transmissions: 1) random SF assignment, 2) adaptive data rate (ADR), and 3) SF selection through machine learning (ML). The main target is to study and determine the most efficient approach as well as to investigate the benefits of using ML techniques in the context of LoRa networks. In this research, a library that enables the communication between ML libraries and OMNeT++ simulator was created. The performance of the approaches is evaluated for different scenarios using the delivery ratio and energy consumption metrics.
The integrity of wireless networks that make up the clear majority of IoT networks lack the inherent security of their wired counterparts. With the growth of the internet of things (IoT) and its pervasive nature in the modern home environment, it has caused a spike in security concerns over how the network infrastructure handles, transmits, and stores data. New wireless attacks such as KeySniffer and other attacks of this type cannot be tracked by traditional solutions. Therefore, this study investigates if wireless spectrum frequency monitoring using interference analysis tools can aid in the monitoring of device signals within a home IoT network. This could be used enhance the security compliance guidelines set forth by OWASP and NIST for these network types and the devices associated. Active and passive network scanning tools are used to provide analysis of device vulnerability and as comparison for device discovery purposes. The work shows the advantages and disadvantages of this signal pattern testing technique compared to traditional network scanning methods. The authors demonstrate how RF spectrum analysis is an effective way of monitoring network traffic over the air waves but also possesses limitations in that knowledge is needed to decipher these patterns. This article demonstrates alternative methods of interference analysis detection.
Underwater wireless sensor networks have become enabling technology for the seawater exploration. Since they raise numerous challenges and problems such as their limited battery and bandwidth, energy-constraint, 3D deployment, and temporary losses of connectivity or link failure, in this paper, a predictive and preemptive cross-layer protocol CLPP-VBF is proposed based on the vector-based forwarding protocol (VBF) for UWSNs, aimed to predict a future loss of connectivity or link failure problems using the Lagrange interpolation method, avoiding unnecessary transmission and rediscovering another new forwarder node in order to guarantee the data packets transmission reliability. The approach is a cooperation between the medium access layer (MAC) and network layer. The study has been implemented and evaluated using the well-known NS2 network simulator with an extension Aqua-sim; the simulation performance shows the merit of CLPP-VBF against VBF in term of delivered packets, average end-to-end delay, and energy consumption.
5G NR (new radio) systems support multiple use cases, namely enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine-type communications (mMTC), to meet the needs of different types of applications. The multi users-downlink packet scheduling (MU-DLPS) is used for the 5G NR radio resource management (RRM). In this paper, the authors show that the radio resource scheduling algorithms, which have been applied to 4G, are also efficient and can be used in 5G networks. In this objective, the authors simulated scheduling schemes in a 5G eMBB environment. The algorithms were developed in C++ for the first time and were simulated using the mmWave model of the NS-3 simulator. Mobility scenarios with fixed and mobile nodes have been implemented. The comparison was made using python programs, newly and specifically developed for the data extraction. The results show that five strategies achieve remarkable values in terms of system throughput and downlink latency.
A MANET is a collection of nodes connected wirelessly that try to converse with each other with no need for any central control or infrastructure establishment. The model of mobility depicts the poignant nature of every node which is mobile in MANETs that is considered to be realistic. It plays a vital role in measuring the performance of MANETs. Mobility is considered to be the prime motive in simulation, because it is a huge influence over the design and network's performance due to limitation in resources and it lead to packet delivery ratio (PDR), varying velocity node energy (NE). Lots of work has been done to improve the above problems. Therefore, there is a requirement of more improvement in this area to enhance overall performance of mobility models. This paper presents a comparative simulation-based analysis of Gauss Markov, Manhattan, and random waypoint mobility models over TCP Newreno that uses a DSDV and AODV routing protocols. Moreover, experiment results and performance analysis have been performed with PDR and NE of the varying number of mobile nodes.
Wireless sensor network is a special kind of ad hoc network characterized by high density, low mobility, and the use of a shared wireless medium. This last feature makes the network deployment easy; however, it is prone to various types of attacks such as sinkhole attack, sybil attack. Many researchers studied the effect of such attacks on the network performance and their detection. Classification techniques are some of the most used end effective methods to detect attacks in WSN. In this paper, the authors focus on sinkhole attack, which is one of the most destructive attacks in WSNs. The authors propose an intrusion detection system for sinkhole attack using support vector machines (SVM) on AODV routing protocol. In the different experiments, a special sinkhole dataset is used, and a comparison with previous techniques is done on the basis of detection accuracy. The results show the efficiency of the proposed approach.
Underwater wireless networks have been the subject of considerable attention in research and development by both academia and industry, while applications are expanding to a wide range of uses, including industrial, scientific, military, and environmental applications. The paper presents a analysis of the underwater wireless sensor network, a system that is promising to reveal the secrets of marine life and other underwater applications. The information about the underwater channel was listed with a focus on communication of both the acoustic and optical kind. Then, the node location strategies and related protocols for routing that can be applied to the desired communication type were discussed briefly. The hard environment and peculiar features of UWSNs are responsible for efficient communication between sensors in UWSNs. This paper proposes a robust and energy-efficient UWSN location-free routing system, based on constraint. RE-PBR takes into account three criteria, including performance, depth, and residual power connections, to balance energy consumption and to produce usable results. The findings of the simulation show that the proposed work decreases travel costs and by using less energy increases the network's life.
Designing sustainable and high-performance wireless multi-core chips requires a matchless tradeoff between many aspects including scalable and reliable architectures implementation which in its turn implies aware-wideband energy-efficient wireless interfaces and adopting innovative straightforward optimization approaches to achieve the optimal configuration with a minimal cost. This paper focuses on investigating various existing designs and methodologies for wireless network on chip (WiNoC) architectures, as well as the different emerging technologies and optimization tools for the design of a robust and reliable WiNoC infrastructure with a special focus on combinatorial optimization meta-heuristics.
In the paper, the problem of using cloudlet-based mobile cloud computing to solve the issues (resource deficiency and limited energy) that require large computing and memory resources on mobile devices has been studied. Energy-saving of mobile devices, resource limitations in mobile devices, and elimination of network delays are analyzed. It is shown that the solution of mobile users' tasks in the cloud leads to the increased battery life of mobile devices and reduces network delays, which is shown by mathematical calculations. The article considers the balanced distribution of the tasks in the cloudlet network. The paper also deals with the selection of cloudlets according to user requirements. The cases for which a cloud application can be loaded by the user were evaluated and a model was proposed using the possible values that determine the importance of cloudlets (user proximity, high reliability, etc.).
Wireless sensor networks (WSNs) have attracted increasing attention during the two last decades. Indeed, their applications target a wide range of fields such as healthcare, industrial control, environmental monitoring, etc. The main challenge of WSNs is the limitation of resources requiring efficient management of energy consumption. Coverage is also considered as one of the critical indicators of the quality of service (QoS) of WSNs. Therefore, the design of routing protocols should focus on energy efficiency and network coverage. In this paper, the authors propose a novel energy-efficient clustering protocol that is more efficient than some prominent routing protocols and offers better coverage of the network than LEACH. The protocol combines the MCL algorithm for cluster formation and a new cluster head selection strategy based on location and residual energy of sensors. Simulation results demonstrate that the proposed protocol is better than other protocols in terms of energy management and extending the lifetime of the network, whereas, it achieves good coverage than LEACH.
Multimedia applications in wireless multimedia sensor networks (WMSNs) demand a high level of quality of service (QoS) requirements. The multipath routing approach is considered as an effective solution to meet these requirements. However, the high-energy consumption in WMSNs is a critical concern for lifetime of network contains sensor nodes with limited battery. Many proposed works have designed multipath routing protocols to provide load balancing between discovered paths, although there is a trade-off between power efficiency and data delivery. This paper proposes a link quality and load balancing multipath geographic routing (LQLB-MGR) protocol for WMSNs. This protocol consists of two phases. The first phase is responsible to find multiple node-disjoint paths with high link quality and the second phase allows load balancing between the discovered paths based on nodal residual energy. Simulation results show that LQLB-MGR provides better performance compared to other protocols.
Energy conservation plays a role important in wireless sensor network (WSN) design. The technique of clustering is one of the approaches to save energy of WSNs; several protocols based on the clustering technique are proposed in the context of energy conservation and maximization of network lifetime. This article proposed a new routing technique to maximize the network lifetime and enhance energy efficiency in WSNs, namely optimal selection of cluster head in the grid (OSCH-Gi). This technique divides the network area into a clustered grid. Each clustered grid has a cluster head (CH) it has been chosen based on the residual energy and distance to the base station of each node. Simulation results indicate that the proposed technique is more effective than other clustering algorithms in terms of the network lifetime and energy consumption.
The quality of service (QoS) evaluation of heterogeneous networks is an interesting area of research. The traditional QoS evaluation methods usually use a set of network-centric parameters for the performance evaluation of these networks. As a result, using such methods it is not possible to report a comprehensive performance review of networks that would include diverse applications and technologies. To resolve this issue, in this research, a novel approach has been proposed that applies dynamic significance weights and unified metrics of the QoS-related parameters to various applications and technologies present within a heterogeneous network. The result analysis shows that by applying this methodical approach, the performance evaluation of heterogeneous networks can be carried out systematically and efficiently.
The detection of unauthorized users can be problematic for techniques that are available at present if the nefarious actors are using identity hiding tools such as anonymising proxies or virtual private networks (VPNs). This work presents computational models to address the limitations currently experienced in detecting VPN traffic. A model to detect usage of VPNs was developed using a multi-layered perceptron neural network that was trained using flow statistics data found in the transmission control protocol (TCP) header of captured network packets. Validation testing showed that the presented models are capable of classifying network traffic in a binary manner as direct (originating directly from a user's own device) or indirect (makes use of identity and location hiding features of VPNs) with high degrees of accuracy. The experiments conducted to classify OpenVPN usage found that the neural network was able to correctly identify the VPN traffic with an overall accuracy of 93.71%. The further work done to classify Stunnel OpenVPN usage found that the Neural Network was able to correctly identify VPN traffic with an overall accuracy of 97.82% accuracy when using 10-fold cross validation. This final experiment also provided an observation of 3 different validation techniques and the different accuracy results obtained. These results demonstrate a significant advancement in the detection of unauthorised user access with evidence showing that there could be further advances for research in this field particularly in the application of business security where the detection of VPN usage is important to an organization.
The set of nodes constituting a minimum connected dominating set (MCDS) in a wireless ad hoc network (WANET) could be considered as the minimum number of nodes that are required to forward a broadcast message so that the message reaches all the nodes in the network. With regards to MCDS construction, we notice that smaller the transmission range for the nodes, the larger the size of the MCDS and vice-versa. Hence, from an energy efficiency point of view, it is imperative to determine the minimum transmission range (we assume uniform transmission range for all the nodes) that would be needed to construct a MCDS of a certain threshold size in WANETs. In this pursuit, we propose a binary search algorithm of logarithmic time complexity to determine the minimum uniform transmission range that would be sufficient to obtain a connected network and construct a MCDS whose size is within a threshold.
A user-centered network model can significantly optimize connectivity issues between a user and the corresponding base station (BS). This article shall evaluate the user-centric (UC) model targeted for Fifth Generation telecommunication systems and will attempt to optimize communication between users and BSs. The authors suggest a resource-aware mechanism that targets improving coverage through the network decoupling into two separate and independent uplink and downlink networks. The mechanism shall fully respect each user's initially requested throughput demands and aims to solve the network user BS association problem with efficient resource management techniques. Simulations revealed that the mechanism perfectly preserves quality of service (QoS) and offers increased data rates in favor of ultimate user coverage, in both scenarios. Additionally, Frequency Range 2 offers an increased amount of resources, both increased data rates and higher amounts of devices that are covered by the overall network.
The authors implement common attacks on a DNS server and demonstrate that DNSSEC is an effective solution to counter DNS security flaws. This research demonstrates how to counter the zone transfer attack via the generation of DNSSEC keys on the name servers which prevent attackers from obtaining a full zone transfer as its request for the transfer without the keys was denied by the primary server. This article also provides a detailed scenario of how DNSSEC can be used as a mechanism to protect against the attack if an attacker tried to perform Cache Poisoning. The authors ultimately show that a DNSSEC server will not accept responses from unauthorised entities and would only accept responses which are authenticated throughout the DNSSEC chain of trust.
Cognitive radio is a form of wireless communication that makes decisions about allocating and managing radio resources after detecting its environment and analyzing the parameters of its radio frequency environment. Decision making in cognitive radio can be based on optimization techniques. In this context, machine learning and artificial intelligence are to be used in cognitive radio networks in order to reduce complexity, obtain resource allocation in a reasonable time and improve the user's quality of service. This article presents recent advances on artificial intelligence in cognitive radio networks. The article also categorizes the techniques presented according to the type of learning—supervised or unsupervised—and presents their applications and challenges according to the tasks of the cognitive radio.
