Search

Routing in vehicular ad hoc networks (VANETs) enables vehicles to communicate for safety and non-safety applications. However, there are limitations in wireless communication that can degrade VANET performance, so it is crucial to optimize the operation of routing protocols to address this. Various routing protocols employed the expected transmission count (ETX) in their operation as one way to achieve the required efficiency and robustness. ETX is used to estimate link quality for improved route selection. While some studies have evaluated the utilization of ETX in specific protocols, they lack a comprehensive analysis across protocols under varied network conditions. This research provides a comprehensive comparative evaluation of ETX-based routing protocols for VANETs using the nomadic community mobility model. It covers a foundational routing protocol, ad hoc on-demand distance vector (AODV), as well as newer variants that utilize ETX, lightweight ETX (LETX), and power-based light reverse ETX (PLR-ETX), which are referred to herein as AODV-ETX, AODV-LETX, and AODV-PLR, respectively. The protocols are thoroughly analyzed via ns-3 simulations under different traffic and mobility scenarios. Our evaluation model considers five performance parameters including throughput, routing overhead, end-to-end delay, packet loss, and underutilization ratio. The analysis provides insight into designing robust and adaptive ETX routing for VANET to better serve emerging intelligent transportation system applications through a better understanding of protocol performance under different network conditions. The key findings show that ETX-optimized routing can provide significant performance enhancements in terms of end-to-end delay, throughput, routing overhead, packet loss and underutilization ratio. The extensive simulations demonstrated that AODV-PLR outperforms its counterparts AODV-ETX and AODV-LETX and the foundational AODV routing protocol across the performance metrics.

Introduction Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy.Methods and analysis This protocol paper describes the data collection procedures for the Optimise Study, an ongoing longitudinal cohort of ~1000 Victorian adults and their social networks. Participants are recruited using snowball sampling with a set of seeds and two waves of snowball recruitment. Seeds are purposively selected from priority groups, including recent COVID-19 cases and close contacts and people at heightened risk of infection and/or adverse outcomes of COVID-19 infection and/or public health measures. Participants complete a schedule of monthly quantitative surveys and daily diaries for up to 24 months, plus additional surveys annually for up to 48 months. Cohort participants are recruited for qualitative interviews at key time points to enable in-depth exploration of people’s lived experiences. Separately, community representatives are invited to participate in community engagement groups, which review and interpret research findings to inform policy and practice recommendations.Ethics and dissemination The Optimise longitudinal cohort and qualitative interviews are approved by the Alfred Hospital Human Research Ethics Committee (# 333/20). The Optimise Study CEG is approved by the La Trobe University Human Ethics Committee (# HEC20532). All participants provide informed verbal consent to enter the cohort, with additional consent provided prior to any of the sub studies. Study findings will be disseminated through public website (https://optimisecovid.com.au/study-findings/) and through peer-reviewed publications.Trial registration number NCT05323799.

Many routing algorithms proposed for IoT are based on modifications on RPL objective functions and trickle algorithms. However, there is a lack of an in-depth study to examine the impact of mobility on routing protocols based on MRHOF and OF0 algorithms. This paper examines the impact of group mobility on these algorithms, also examines their ability in distributing the load and the impact of varying traffic with the aid of simulations using the well-known Cooja simulator. The two algorithms exhibit similar performance for various metrics for low traffic rates and low mobility speed. However, when the traffic rate becomes relatively high, OF0 performance merits appear, in terms of throughput, packet load deviation, power deviation, and CPU power deviation. The mobility with higher speeds helps MRHOF to enhance its throughput and load deviation. The mobility allowed MRHOF to demonstrate better packets load deviation.

Routing in vehicular ad hoc networks (VANETs) enables vehicles to communicate for safety and non-safety applications. However, there are limitations in wireless communication that can degrade VANET performance, so it is crucial to optimize the operation of routing protocols to address this. Various routing protocols employed the expected transmission count (ETX) in their operation as one way to achieve the required efficiency and robustness. ETX is used to estimate link quality for improved route selection. While some studies have evaluated the utilization of ETX in specific protocols, they lack a comprehensive analysis across protocols under varied network conditions. This research provides a comprehensive comparative evaluation of ETX-based routing protocols for VANETs using the nomadic community mobility model. It covers a foundational routing protocol, ad hoc on-demand distance vector (AODV), as well as newer variants that utilize ETX, lightweight ETX (LETX), and power-based light reverse ETX (PLR-ETX), which are referred to herein as AODV-ETX, AODV-LETX, and AODV-PLR, respectively. The protocols are thoroughly analyzed via ns-3 simulations under different traffic and mobility scenarios. Our evaluation model considers five performance parameters including throughput, routing overhead, end-to-end delay, packet loss, and underutilization ratio. The analysis provides insight into designing robust and adaptive ETX routing for VANET to better serve emerging intelligent transportation system applications through a better understanding of protocol performance under different network conditions. The key findings show that ETX-optimized routing can provide significant performance enhancements in terms of end-to-end delay, throughput, routing overhead, packet loss and underutilization ratio. The extensive simulations demonstrated that AODV-PLR outperforms its counterparts AODV-ETX and AODV-LETX and the foundational AODV routing protocol across the performance metrics. [ABSTRACT FROM AUTHOR]

Smart Grid (SG) is a major electricity trend expected to replace traditional electricity systems. SG has faster response to electricity malfunctions and improved utilization of consumed power, and it has two-way communication between providers and consumers. However, SG is vulnerable to attacks and requires robust authentication techniques to provide secure authenticity for its components. This paper analyses previous literature, comprising 27 papers on the status of SG authentication techniques, main components, and kinds of attacks. This paper also highlights the main requirements and challenges for developing authentication approaches for the SG system. This can serve as useful guidance for the development and deployment of authentication techniques for SG systems and helps practitioners select authentication approaches applicable to system needs.

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(24). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption; it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES.

SQL injection is a type of database-targeted attack for data-driven applications. It is performed by inserting malicious code in the SQL query to alter and modify its meaning, enabling the attacker to retrieve sensitive data or to access the database. Many techniques have been improved and proposed to detect and mitigate these types of attacks. This paper provides a systematic review for a pool of 60 papers on web applications' SQL injection detection methods. The pool was selected using a developed searching and filtering methodology for the existing literature based on scholar databases (IEEE, ScienceDirect, and Springer) with the aim to provide specific answering for several research questions in the area of SQL injection detection. This provides a basis for the design and use of effective SQL injection detection methods. [ABSTRACT FROM AUTHOR]