Hyperbolic routing (HR) techniques are known to provide powerful and efficient packet delivery capabilities for Named Data Networking (NDN) architectures. HR is often implemented using various greedy forwarding techniques. However, the underlying implementation choices for HR are not well understood and not always well defined. This paper addresses this issue by developing a taxonomy for greedy HR and then evaluating the performance of different greedy strategies. The evaluation was conducted using a number of different methods for caching and approaches for interest collapsing. Among the results is that the standard method for returning data to a consumer - simple reverse path forwarding - is often suboptimal in terms of performance. To address this issue, this paper proposes and evaluates a more aggressive producer-to-consumer packet return mechanism. The results benefit future work using HR in NDN.

Preserving privacy is an undeniable benefit to users online. However, this benefit (unfortunately) also extends to those who conduct cyber attacks and other types of malfeasance. In this work, we consider the scenario in which Privacy Preserving Technologies (PPTs) have been used to obfuscate users who are communicating online with ill intentions. We present a novel methodology that is effective at deobfuscating such sources by synthesizing measurements from key locations along protocol transaction paths. Our approach links online personas with their origin IP addresses based on a Pattern of Life (PoL) analysis, and is successful even when different PPTs are used. We show that, when monitoring in the correct places on the Internet, DNS over HTTPS (DoH) and DNS over TLS (DoT) can be deobfuscated with up to 100% accuracy, when they are the only privacy-preserving technologies used. Our evaluation used multiple simulated monitoring points and communications are sampled from an actual multiyear-long social network message board to replay actual user behavior. Our evaluation compared plain old DNS, DoH, DoT, and VPN in order to quantify their relative privacy-preserving abilities and provide recommendations for where ideal monitoring vantage points would be in the Internet to achieve the best performance. To illustrate the utility of our methodology, we created a proof-of-concept cybersecurity analyst dashboard (with backend processing infrastructure) that uses a search engine interface to allow analysts to deobfuscate sources based on observed screen names and by providing packet captures from subsets of vantage points.

Using topological summary tools such as persistence landscapes have greatly enhanced the practical usage of topological data analysis to analyze large-scale, noisy, and complex datasets. A central element of persistence landscape usage involves computing the top- k landscapes. This article presents a novel output-sensitive plane sweep algorithm for computing the top- k persistence landscapes in optimal time and space: significantly outperforming previous algorithms. Our algorithm can determine in optimal O ( n * log ( n ) ) if a given birth-death pair appears in the top- k landscapes. The runtime performance of the approach on a botnet dataset and several synthetically generated point cloud topologies, showing that the algorithm can achieve significant speedups for these datasets due to its better algorithmic design. The speedups seen range from slightly worse (in some extreme examples) to equal compared to previous works while returning exactly the same output and is significantly faster when filtering is used (15x for birth-death pairs when removing 75% of birth-death pairs). Filtering is shown to maintain machine learning performance on both synthetically generated and real world datasets while providing orders of magnitude speedup depending on how intensive of filtering is done. Due to the introduced algorithm’s algorithmic design, the speedup seen is greater when filtering using the introduced birth-death filtering algorithm. The software is freely provided in Rust with Python bindings online.

Information Centric Networking (ICN) represents an emerging paradigm for information distribution, whereby data is identified by its name rather than its network location. ICNs are expected to increase system scalability and throughput through schemes such as in-network caching. Due to dramatic overhead reduction, novel Hyperbolic Routing (HR) methods are being considered to support ICN networks. This paper considers the use of HR to support ICN caching in relationship to consumer privacy and routing performance. We first describe general cache privacy policies, and then outline a procedure for assigning hyperbolic coordinates. Next, we present an algorithm to construct cache clusters using hyperbolic Voronoi diagrams. Finally, we compare the performance of HR to traditional Link State (LS) routing protocols under a number of caching scenarios. Our results show that for applications such as content retrieval HR has competitive performance with LS with the advantage of reduced overhead.

It has been established that in-network caching in an Information-Centric Network (ICN) environment significantly reduces required bandwidth and content retrieval delay, and reduces load on content producers. However, malicious actors masquerading as legitimate consumers can probe cache contents and use the resultant data to map content objects to, and thereby violate the privacy of, the consumer(s) who requested them. Existing mitigation approaches suffer a direct trade-off between privacy and utility; the two are diametrically opposed, and prioritizing either rapidly degrades its counterpart. This paper presents a collaborative caching approach with provable privacy and utility guarantees that instead monotonically increase as a function of one another, growing in tandem. Our proposed scheme preserves all true cache hits to utilize in-network caching as efficiently as possible. We have evaluated our method against a number of other in ICN caching policies for a variety of workloads and topologies. Our results show that our technique delivers high cache hit ratios and minimizes interest satisfaction delay while offering provable privacy guarantees.

The previous work on connection driven topology control has shown that it has significant potential to reduce energy consumption of Wireless Sensor Networks (WSNs). Dynamic Modulation Scaling (DMS) which is a technique that manages transmission power levels in order to change the number of bits encoded per symbol has a direct impact on connection driven topology control. In this paper we investigate the transmission scheduling of multi-hop real-time WSNs equipped with DMS enabled radio chips while taking the effect of DMS on topology control into account. To our best knowledge, this is the first paper that addresses this issue. The current work on DMS enabled WSN tend to rely on theoretical DMS models to predict network performance metrics. However, there is little, if any, work that is based upon empirically verified network performance outcomes using DMS especially on its effect on connection driven topology control. This paper fills this gap by using GNU Radio and Software Defined Radio hardware to show how to emulate DMS in low power wireless systems and measure the impact of varying Signal-to-Noise levels, distance and elevation on throughput and delivery rates for different DMS control strategies. Next, we present the Mixed Integer Nonlinear Optimization Problem of minimizing energy consumption of DMS enabled connection driven topology control on real-time WSNs. Lastly, we present two polynomial time heuristics and compare their performance against the optimal solution.

Large-scale Information Centric Disruption Tolerant Networks (ICDTNs) are now being evaluated as a powerful system paradigm for data sharing when existing communication infrastructures are compromised. The effectiveness of an ICDTN is largely dependent upon efficient and scalable data advertising mechanisms, yet this problem has until recently not been extensively studied. This paper proposes a solution to the data advertising problem that is based upon random linear network coding. We show how to encode, decode, and use Named Data Networking advertising structures in an ICDTN environment. We have compared our approach to the standard flooding techniques under a variety of network sizes, communication conditions, node densities and mobility patterns. Our results show that our approach is both highly scalable and can significantly decrease the time for advertisement message delivery.

MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON's architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.

Detecting and classifying device types and their long term communication patterns and anomalies in massive, noisy and anonymized Internet-of-things (IoT) data sets is a challenging problem. Recent advances in computational approaches for Topological Data Analysis (TDA), including the technique of persistence homology, appear to offer tremendous possibles for understanding highly complex IoT data sets. This paper presents the results of our use of TDA to understand a data set captured over 9 months of hundreds of interacting IoT devices situated in multiple residential settings. The data set is noisy, incomplete and subject to multiple Pattern-of-Life (PoL) fluctuations. We treated the data set as a collection of multi-attribute time series and performed several types of IoT classification experiments. We compared our results to other single and multi-attribute techniques for time series analysis. The outcome was that, as compared to these other standard methods, TDA does particularly well for classifying incomplete, noisy and PoL dependent IoT data.

Information Centric Disruption Tolerant Networks (ICDTNs) have recently been proposed as a powerful approach to provide effective data and information sharing when existing communication infrastructures are degraded or destroyed. This paper explores using a hybrid approach for ICDTN construction using Named Data Networking (NDN) and DTN architectures that avoids the need to heavily modify existing implementations. We analyze different design choices and argue that a geographically aware architecture is particularly effective for many ICDTN applications. To support this claim we evaluate “geo” versus “node” aware NDN implementations using a number of mobility models, network sizes, DTN forwarding methods, and caching in disaster scenarios. Our results demonstrate the scalability and effectiveness of our approach with the appropriate selection of geo information, name advertisement and caching.

This paper describes Distributed MASON, a distributed version of the MASON agent-based simulation tool. Distributed MASON is architected to take advantage of well known principles from Parallel and Discrete Event Simulation, such as the use of Logical Processes (LP) as a method for obtaining scalable and high performing simulation systems. We first explain data management and sharing between LPs and describe our approach to load balancing. We then present both a local greedy approach and a global hierarchical approach. Finally, we present the results of our implementation of Distributed MASON on an instance in the Amazon Cloud, using several standard multi-agent models. The results indicate that our design is highly scalable and achieves our expected levels of speed-up.

Many applications running over low-power and lossy wireless networks and wireless sensor networks (WSNs) rely heavily on a number of all-to-all communication primitives for services such as data aggregation, voting and consensus. Starting with the Chaos system, synchronous transmission-based broadcasting gossip protocols are now recognized as a technique for enabling efficient all-to-all communications in WSNs. However, despite their effectiveness, there has been relatively little analysis of this class of synchronous broadcasting gossip protocols (SBGPs). In this paper, we address this void by providing a basic theoretical framework for analysis SBGPs. Based on our derived theoretical results and previous experimental measurements, we show that the key for better performance is to increase the network connectivity as much as possible while limiting the number of concurrent transmitters. As a proof of the concept, we propose a multi-radio approach of the SBGP to achieve this purpose. We compare four multi-radio schemes of SBGP with a single radio SBGP through simulation and result has shown the convergence latency can be reduced up to 42% by utilizing multiple radios.

Dynamic Modulation Scaling (DMS) is a well-known mechanism that can effectively exploit the tradeoff between communication time and energy consumption. In recent years a number of studies have suggested that DMS techniques can reduce energy consumption while maintaining performance objectives in low-power wireless transmission technologies such as those defined in IEEE 802.15.4. These studies tend to rely on theoretical or simulation DMS models to predict network performance metrics. However, there is little, if any, work that is based upon empirically verified network performance outcomes using DMS. This paper fills that gap. Our contribution is four-fold; first, using GNU~Radio and SDR hardware we show how to emulate DMS in low power wireless systems. Second, we measure the impact of varying Signal-to-Noise levels on throughput and delivery rates for different DMS control strategies. Third, using DMS we quantify the impact of distance and finally, we measure the impact of different elevations between sender and receiver on network performance. Our results provide an empirical basis for future work in this area.

Wireless Sensor Networks (WSNs) are increasingly used in industrial applications such as the Internet-of-Things, Smart City technologies and critical infrastructure monitoring. Industrial WSNs often operate in a cluster or star configuration. To ensure real-time and predictable performance, link access is typically managed using time-slotted superframe methods. These methods generally use static and potentially inefficient slot assignments. In this paper, we propose to dynamically readjust time slot lengths as a technique to minimize overall energy consumption. Our approach combines real-time performance guarantees with energy conservation methods through a set of dynamic modulation based adaptive packet transmission scheduling algorithms that are designed to reclaim unused slot times. To support our reclaiming method in a wireless environment we introduce a novel low-power listening technique called reverse-low-power listening (RLPL) as part of an overall Hybrid Low-Power Listening (HLPL) protocol. We evaluate our algorithms using Castalia simulator against an oracle-based approach, and show that our dynamic slot reclaiming approach, coupled with HLPL, can introduce substantial power savings without sacrificing real-time support which may be a new approach towards improving industrial wireless standards.

Distributed Denial of Service (DDoS) attacks serve to diminish the ability of the network to perform its intended function over time. The paper presents the design, implementation and analysis of a protocol based upon a technique for address agility called DDoS Resistant Multicast (DRM). After describing the our architecture and implementation we show an analysis that quantifies the overhead on network performance. We then present the Simple Agile RPL multiCAST (SARCAST), an Internet-of-Things routing protocol for DDoS protection. We have implemented and evaluated SARCAST in a working IoT operating system and testbed. Our results show that SARCAST provides very high levels of protection against DDoS attacks with virtually no impact on overall performance.

In this article, we consider clustered wireless sensor networks where the nodes harvest energy from the environment. We target performance-sensitive applications that have to collectively send their information to a cluster head by a predefined deadline. The nodes are equipped with Dynamic Modulation Scaling (DMS)-capable wireless radios. DMS provides a tuning knob, allowing us to trade off communication latency with energy consumption. We consider two optimization objectives, maximizing total energy reserves and maximizing the minimum energy level across all nodes. For both objectives, we show that optimal solutions can be obtained by solving Mixed Integer Linear Programming problems. We also develop several fast heuristics that are shown to provide approximate solutions experimentally.

The advantages of using of the mobile sinks (MSs) to perform data collection from Wireless Sensor Networks (WSNs) are now widely recognized. This is because the MS data collectors can service isolated systems and reduce energy expenditures by minimizing the need for multi-hop networking. However, it remains a challenging problem to support collection activities efficiently within the relevant class of predictable but uncontrollable MSs. This paper presents MuTrans, a novel multi-channel protocol that uses both clustering and network coding techniques to increase the reliability and reduce the latency of data collection in predictable and uncontrollable MS systems. A fairness data uploading scheduling to a mobile collector is presented based on assigning a method for load balancing. The implementation is described by using multiple channels for increased throughput and incorporating network coding for improved reliability. Our evaluation of data aggregation in the presence of packet errors shows that MuTrans can significantly reduce the latency for data collection, thus providing strong support for mobile data collection.

Wireless Sensor Networks (WSN) have been increasingly applied to industrial monitoring and control, where soft Quality of Service guarantees under worst-case scenarios are often required. Many performance analysis frameworks including Network Calculus are proposed to derive those guarantees. Those frameworks were originally designed for wired network and typically do not consider energy constraints, which play a crucial role in the operation of wireless sensor networks. In this paper, a novel model is proposed to integrate energy harvesting into the network calculus framework to stochastically bound the worst-case performance. In our framework, energy is converted into tokens to stochastically bound the service provided to the traffic. Meanwhile, behaviors of nodes during the situations of energy overflow and underflow are accurately characterized by two virtual queues. Various performance metrics are analyzed and bounded for a single node and for a tree-based data collection wireless sensor network. Additionally, procedures are presented to show the model instantiation for a specific wireless sensor network based on hardware parameters and empirically obtained energy harvesting traces.

A large number of embedded wireless systems must handle complex and time-varying computational and communication workloads. Further, a significant number of these systems support real-time applications. Most of the existing energy management studies for such systems have focused on relatively simple scenarios that assume deterministic workloads, and only consider a limited range of energy management techniques, such as Dynamic Voltage Scaling (DVS). Our paper addresses these deficiencies by proposing a general purpose probabilistic workload model for computation and communication. To account for the importance of radio energy consumption, we also analyse Dynamic Modulation Scaling (DMS), an often overlooked method for energy management. We define several energy control algorithms, including an optimal combined DVS–DMS approach, and evaluate these algorithms under a wide range of workload values and hardware settings. Our results illustrate the benefits of joint power control algorithms.

Denial of Service (DoS) attacks serve to diminish the ability of the network to perform its intended function over time. As DoS attacks may permeate the veil of the Internet of Things (IoT) devices from the broader internet, it is vital that these devices are able to mitigate these attacks. If such a network of IoT devices were to fail during time of need, lives of patients, war fighters, or the sustainability of manufacturing processes may be placed at risk. This work implements and provide initial assessment on a strategy to mitigate a resource exhaustion DoS attack using a network multicast service, wherein such an attack may target the critical energy reserves of low power devices. The Simple Agile RPL Multicast (SARCAST) concept implemented herein uses an agile addressing scheme to reduce the efficacy of multicast-based DoS attacks on IoT devices.