Optimizing Source Anonymity Of Wireless Sensor Networks Against Global Adversary Using Fake Packet Injections

Wireless Sensor Networks WSNs have been utilized for many applications such as tracking and monitoring of endangered species in a national park, soldiers in a battlefield, and many others, which require anonymity of the origin, known as the Source Location Privacy (SLP). The aim of SLP is to prevent unauthorized observers from tracing the source of a real event (an asset) by analyzing the traffic of the network. We develop the following six techniques to provide anonymity: Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD), Controlled Dummy Adaptive Distribution (CAD), Exponential Dummy Adaptive Distribution (EDAD), Exponential Dummy Adaptive Distribution Plus One (EDADP1), and Exponential Dummy Adaptive Distribution Plus Two (EDADP2). Moreover, an enhanced version of the well-known FitProbRate technique is also developed. The purpose of these techniques is to overcome the anonymity problem against a global adversary model that has the capability of analyzing and monitoring the entire network. We perform an extensive verification of the proposed techniques via simulation, statistical, and visualization approaches. Three analytical models are developed to verify the performance of our techniques: A Visualization model is performed on the simulation data to confirm anonymity. A Neural Network model is developed to ensure that the introduced techniques preserve SLP. In addition, a Steganography model based on statistical empirical data is implemented to validate the anonymity of the proposed techniques. The Simulation demonstrates that the proposed techniques provide a reasonable delay, delivery ratio, and overhead of the real event's packets while keeping a high level of anonymity. Results show that the improved version of FitProbRate massively reduces the number of operations needed to detect the distribution type of a data sequence despite the number of intervals when compared to the original. A comprehensive comparison between EDADP1, EDADP2, and FitProbRate in terms of the average delay, anonymity level, average processing time, Anderson-Darling test, and polluted scenarios is conducted. Results show that all three techniques have a similar performance regarding the average delay and Anderson-Darling test. However, the proposed techniques outperform FitProbRate in terms of anonymity level, average processing time, and polluted scenarios. WSN applications that need privacy can select the suitable proposed technique based on the required level of anonymity with respect to delay, delivery ratio, and overhead

Source Anonymity in WSNs using Real/Fake packet Injections

Many of Wireless Sensor Networks (WSNs) applications such as tracking and monitoring endangered species, and/or military applications in areas of interest require anonymity of the origin known as Source Location Privacy (SLP). The aim is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic on the network. Three different techniques: Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) are introduced to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques improve delivery ratio and reduce the delay and overhead of a real event's packets while keeping a high level of anonymity

Source Anonymity in WSNs against Global Adversary Utilizing Low Transmission Rates with Delay Constraints

Wireless sensor networks (WSN) are deployed for many applications such as tracking and monitoring of endangered species, military applications, etc. which require anonymity of the origin, known as Source Location Privacy (SLP). The aim in SLP is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic in the network. Previous approaches to SLP such as Fortified Anonymous Communication Protocol (FACP) employ transmission of real or fake packets in every time slot, which is inefficient. To overcome this shortcoming, we developed three different techniques presented in this paper. Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) were developed to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Most of the current techniques try to prevent the adversary from perceiving the location and time of the real event whereas our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques provide reasonable delivery ratio, delay, and overhead of a real event's packets while keeping a high level of anonymity. Three different analysis models are conducted to verify the performance of our techniques. A visualization of the simulation data is performed to confirm anonymity. Further, neural network models are developed to ensure that the introduced techniques preserve SLP. Finally, a steganography model based on probability is implemented to prove the anonymity of the techniques.https://doi.org/10.3390/s1607095

TERP: A Trusted and Energy Efficient Routing Protocol for Wireless Sensor Networks (WSNs)

Recently, Wireless Sensor Networks (WSNs) have got researchers attention due to its various useful and helpful applications in the real world with low cost sensors. The task of the sensors is to collect data from the environment and send it to the central node (sink node). However, the power is limited in these sensors and therefore it has a limited lifetime which is a big deal in WSNs. Another important issue in WSNs is the level of security. Since these sensor nodes exchange and transmit data among the network, the security of the data can be at risk. Hence, In this poster, we propose a novel trusted and energy efficient routing protocol (TERP), which is based on the Destination Sequenced Distance Vector Protocol (DSDV). TERP can avoid any malicious nodes (untrusted nodes) and thus increase the security level in the network, and decrease the power consumption level

Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey

Source anonymity in wireless sensor networks (WSNs) becomes a real concern in several applications such as tracking and monitoring. A global adversary that has sophisticated resources, high computation and full view of the network is an obvious threat to such applications. The network and applications need to be protected and secured to provide the expected outcome. Source anonymity is one of the fundamental WSNs security issues. It is all about preventing the adversary from reaching the origin by analyzing the traffic of the network. There are many methods to provide source anonymity, which is also known as Source Location Privacy (SLP). One of these methods is based on dummy packets. The basic notion is to inject the network with dummy packets to confuse the adversary about the location of the transmitting source node. This paper provides a survey of protocols for anonymity that use dummy packet injections. We discuss each technique from the point of their advantages and disadvantages. Further, We provide a comparison for the most promising techniques provided in the literature which use dummy packet injections. A comparison for the adversary assumptions and capabilities will be provided as well.http://dx.doi.org/10.3390/electronics710025

A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an average localization error of 0.51 m and a user acceptability level of 86.1%

A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an average localization error of 0.51 m and a user acceptability level of 86.1%

Association between Obesity and COVID-19: Insights from Social Media Content

The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity