Wireless Interference Analysis for Home IoT Security Vulnerability Detection

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.

Sinkhole Attack Detection-Based SVM In Wireless Sensor Networks

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.

A Cross-Layer Predictive and Preemptive Routing Protocol for Underwater Wireless Sensor Networks Using the Lagrange Interpolation

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.

Performance Analysis of TCP Newreno Over Mobility Models Using Routing Protocols in MANETs

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.

MAC Optimization Based on the Radio Resource Allocation in a 5G eMBB System Simulated in the MmWave Model

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 Comparative Study of Machine Learning Models for Spreading Factor Selection in LoRa Networks

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.

Networks of Underwater Sensor Wireless Systems

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.

Link Quality and Load Balancing Multipath Geographic Routing for Wireless Multimedia Sensor Networks

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.

Strategy for Reducing Delays and Energy Consumption in Cloudlet-Based Mobile Cloud Computing

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.).

A Low Energy MCL-Based Clustering Routing Protocol for Wireless Sensor Networks

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.