The world of finance and decentralized applications has undergone a revolution with the advent of blockchain technology, resulting in the emergence of various blockchain platforms. This underscores the necessity for conducting exchanges between different blockchains to facilitate blockchain interoperability. However, the challenges linked to the scalability of individual blockchains pose negative implications during inter-chain exchanges. A prominent solution to tackle the scalability challenge in a blockchain, particularly in terms of throughput, is a Payment Channel Network. However, creating such a solution for facilitating inter-blockchain exchange presents significant challenges, as it demands atomic state updating on two different blockchains. In this paper, we introduce a novel, fully distributed mechanism for establishing cross-chain payment channels (CCPC), designated as Tombolo protocol, designed to operate seamlessly across different blockchains. Furthermore, we provided the sequence of steps that the user should follow to facilitate further exchange without contacting either of the involved blockchains. Additionally, we showcase its resilience against malicious behaviour, specifically in situations where a participant attempts to close the channel using an outdated state or becomes inactive during exchanges. Furthermore, we have demonstrated the viability of Tombolo by implementing it on an Ethereum Virtual Machine-based blockchain using Solidity v0.8.0 based smart contracts. Through comprehensive experimentation and evaluation, we illustrate that CCPC can be established with approximately 2.5 times the gas utilization and around twice the time overhead compared to Raiden, a state-of-the-art intra-chain channel.

Permissionless blockchain consensus protocols have been leveraged for defining decentralized economies for the (commercial or private) trade of virtual and physical assets, using cryptocurrencies. In most instances, the assets being traded are regulated , which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formalize proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in illegal activities in the associated crypto-economies. In this contribution, unlike existing non-consensus solutions, which are prone to be more compute-time and audit-time intensive, we present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain consensus. As per our regulatory framework, we derive, through a stochastic game analysis, block proposal strategies under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that when a majority of the consensus protocol participants are licensed by the regulator to propose legal transactions, there exists a fair consensus execution policy to maximize the legal transaction throughput in the blockchain network.

We consider the problem of correctly identifying the mode of a discrete distribution $\mathcal{P}$ with sufficiently high probability by observing a sequence of i.i.d. samples drawn from $\mathcal{P}$. This problem reduces to the estimation of a single parameter when $\mathcal{P}$ has a support set of size $K = 2$. After noting that this special case is handled very well by prior-posterior-ratio (PPR) martingale confidence sequences (Waudby-Smith and Ramdas, 2020), we propose a generalisation to mode estimation, in which $\mathcal{P}$ may take $K \geq 2$ values. To begin, we show that the "one-versus-one" principle to generalise from $K = 2$ to $K \geq 2$ classes is more efficient than the "one-versus-rest" alternative. We then prove that our resulting stopping rule, denoted PPR-1v1, is asymptotically optimal (as the mistake probability is taken to 0). PPR-1v1 is simple and computationally light, and incurs significantly fewer samples than competitors even in the non-asymptotic regime. We demonstrate its gains in two practical applications of sampling: election forecasting and verification of smart contracts in blockchains.

Cryptocurrency networks are a promising infrastructure for pseudonymous online payments. However, low throughput has prevented their widespread acceptance. A promising solution to scale throughput is the Payment channel network (PCN), exemplified by the Lightning Network (LN), that uses a network of off-chain bidirectional payment channels between parties that wish to transact often. Since payments use the shortest paths with sufficient funds over this network, channel balances get exhausted in the direction transactions flow and eventually become unidirectional. This results in transactions failing and consequently a lower transaction success ratio. Our observations on the production LN show that over 63% of the channels lose over 80% of the channel balance in one direction over time, which makes the success ratio of a real-world workload drop from 71% to 29%. A unidirectional channel along a path results in a failure message back to the source that recomputes the path, excluding the failed channel and reattempts the transaction, thus adding to the completion latency even for those transactions that do complete.We propose REBAL, a distributed re-balancing mechanism, and a new routing scheme to address the above issues. REBAL maximizes the extent to which channels can be re-balanced across the entire network. REBAL addresses the completion latency issue by re-routing transactions from intermediate nodes around a unidirectional channel rather than propagating the failure back to the source.Our comprehensive evaluation of REBAL shows that the success ratio improves from 30.18% to 79.54% and success volume from 3.98% to 29.99% for a real-world workload derived from the Ripple network, without adversely impacting the transaction latency. Even at very high transaction rates, REBAL outperforms Lightning Network Daemon (LND- a Golang implementation of LN) (12%) with a success ratio of 43.76%.

A Blockchain system such as Ethereum is a peer to peer network where each node works in three phases: creation, mining, and validation phases. In the creation phase, it executes a subset of locally cached transactions to form a new block. In the mining phase, the node solves a cryptographic puzzle (Proof of Work-PoW) on the block it forms. On receiving a block from another peer, it starts the validation phase, where it executes the transactions in the received block in order to ensure all transactions are valid. This execution also updates the blockchain state, which must be completed before creating the next block. A long block validation time lowers the system's overall throughput and brings the well known Verifier's dilemma into play. Additionally, this leads to wasted mining power utilization (MPU).

We present a novel scheme called Decentralized Attestation for Device Swarms (DADS), which is, to the best of our knowledge, the first to accomplish decentralized attestation in device swarms. Device swarms are smart, mobile, and interconnected devices that operate in large numbers and are likely to be part of emerging applications in Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoTs). Swarm devices process and exchange safety, privacy, and mission-critical information. Thus, it is important to have a good code verification technique that scales to device swarms and establishes trust among collaborating devices. DADS has several advantages over current state-of-the-art swarm attestation techniques: It is decentralized, has no single point of failure, and can handle changing topologies after nodes are compromised. DADS assures system resilience to node compromise/failure while guaranteeing only devices that execute genuine code remain part of the group. We conduct performance measurements of communication, computation, memory, and energy using the TrustLite embedded systems architecture in OMNeT++ simulation environment. We show that the proposed approach can significantly reduce communication cost and is very efficient in terms of computation, memory, and energy requirements. We also analyze security and show that DADS is very effective and robust against various attacks.

In this paper a novel cloud-based Emergency Warning System (EWS) is described, using India as a primary case study. India is a country with nearly two dozen officially recognized languages and divergent communication technologies spread across a large geographical area. As of yet, this diversity has made the deployment of a single nationwide EWS near impossible. To address this deficiency, we have developed an EWS with adaptation as a central design tenet. This adaptation occurs on three levels: Dissemination adaptation addresses civilians' heterogeneous communication technologies; information adaptation morphs warnings to best reflect the capabilities of these communication technologies and users; while presentation adaptation is used to render information to the user in the most appropriate manner. We have developed a full cloud-based prototype, including the full EWS infrastructure and a civilian Android app.

The authors present a novel frequency division multiplexing scheme which can generate a signal whose worst‐case peak‐to‐average power ratio (PAPR) is tunable by an input parameter; they call this scheme tunable PAPR frequency division multiplexing (TP‐FDM). Special cases of TP‐FDM are orthogonal FDM (OFDM) and single carrier (SC) modulation. Unlike several other proposed PAPR reduction schemes, TP‐FDM requires no per‐symbol side information to be sent from transmitter to receiver. TP‐FDM has overall computational requirements almost the same as OFDM. The author's study of the symbol error rate (SER) of TP‐FDM in a Rayleigh fading channel shows that higher PAPR signals give a better SER performance. Thus, instead of choosing either of the extremes, OFDM and SC modulation, as is currently done in some cellular systems, one can tune the PAPR with TP‐FDM to the maximum suitable for the transmitter and receiver device electronics, so as to minimise SER. They compare and contrast TP‐FDM with transform precoded OFDM methods in terms of SER and PAPR. They also extend TP‐FDM to a multi‐user scenario.

Indoor maps are highly essential for indoor positioning and location-based services. Applications providing navigation support to users are rendered useless without a map of the vicinity being available. Presently, floorplans of public locations are collected and maintained by designated organizations using methods that require excessive manual intervention. This process of creating a database of indoor maps is neither efficient nor scalable to the practically infinite number of public indoor places around the world. In this paper, we present a crowdsourcing algorithm to automatically create floorplans of buildings with zero prior information. The algorithm leverages the positioning data shared by pedestrians using smartphone-based navigation systems in the building. It expects only position fixes and associated uncertainties from the navigation systems and does not depend on any particular navigation algorithm. The available positioning data in a completely unknown building is essentially PDR-based and is known to be prone to high amounts of accumulated error primarily due to the lack of reliable error resetting techniques. The presented algorithm takes into account the possibility of such highly erroneous motion traces of pedestrians while trying to generate map as accurately as possible. As an added merit, the algorithm does not depend on the availability of Wi-Fi access points.

Broadband penetration in developing countries such as India is still very low, especially in rural areas, because of the prohibitive cost of laying cable and fiber. Recently, wireless solutions based on Wi-Fi have been proposed as a cost-effective solution to bridge the last-mile problem. However, Wi-Fi normally operates in the 2.4 GHz band and above, where signal propagation is not as good as in sub-GHz bands such as terrestrial TV bands. Using TV white spaces for broadband connectivity can be game changing, bringing down the cost of such networks to an affordable level. Few studies exist, however, which characterize sub-GHz spectrum usage in India. In this paper, we make two main contributions. First, we present results of several day-long spectrum measurements in the sub-GHz band made at different urban and rural locations in the National Capital Region (i.e. near New Delhi) of India. Second, we have developed an inexpensive spectrum measurement setup based on a commercially available hand-held spectrum analyzer called RF Explorer. Our main findings are that about 85% of the TV band in the 470-698 MHz range are not used in the heart of urban Delhi, and as much as 95% are unused in rural areas. At any point in time, the largest contiguous TV white space varies between 66 MHz to 136 MHz in an urban location, and between 51 MHz and 242 MHz in a rural location. This suggests the need for development of very wideband, and low-cost wireless solutions which can exploit these valuable white spaces to provide affordable rural connectivity.

This paper presents a waist-worn Pedestrian Dead Reckoning (PDR) System that requires minimal end-user calibration. The PDR system is based on an Inertial Measurement Unit (IMU) comprising of a tri-axial accelerometer, a tri-axial magnetometer and a tri-axial gyroscope. We propose a novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them. Accelerometer measurements are used to detect step events and to estimate step lengths. Experimental results show that a relative distance error of about 3% to 8% can be obtained using our methods.

In this paper, we demonstrate that anomalies in Internet traces can have a significant impact on semi-experiments that are designed to determine the causes of scaling behavior of traffic. A semi-experiment involves artificially modifying a specific aspect of a trace and studying the resulting change in scaling behavior. We demonstrate using MAWI traces that semi-experiments performed without addressing the presence of anomalies give insights that contradict widely accepted theories regarding Internet traffic scaling behavior. For example, a direct semi-experimental analysis seems to suggest that removing large flows does not result in the removal of LRD behavior and that the scaling behavior of MAWI traces is the same before and after the removal of the large flows. This observation hence challenges the well-known hypothesis that the heavy-tailed distribution of flow sizes is the primary factor causing correlation at large time-scales. To mitigate the impact of anomalies, we couple the semi-experiments with a recently proposed sketch-based procedure for robust estimation of scaling behavior. We term these ``robust semi-experiments". Our analysis shows that using a robust estimation procedure enables a meaningful semi-experimental analysis and that the conclusions drawn from the robust semi-experiments agree with well-established theories regarding Internet traffic scaling behavior.

The immense popularity of new-age ¿Web 2.0¿ applications such as YouTube, Flickr, and Facebook, and non-Web applications such as peer-to-peer (P2P) file sharing, voice over IP, online games, and media streaming have significantly altered the composition of Internet traffic with respect to what it was a few years ago. In light of these changes, this paper revisits Internet traffic characteristics and models that were proposed when ¿traditional¿ Web traffic was the largest contributor to Internet traffic. Specifically, we study whether or not the following characteristics, namely: (1) traffic is self-similar and long-range dependent, and (2) traffic can be approximated by Poisson at smaller time scales, are still valid. Our experiments on recent traces show that these traffic characteristics continue to hold. We further argue that current Internet traffic can be viewed to have two key constituents, namely Web+ and P2P+; Web+ traffic consists of traffic from both Web 1.0 and Web 2.0 applications; P2P+ traffic consists largely of traffic from P2P applications and other non-Web applications excluding applications on well-known ports such as FTP and SMTP. We then show that both Web+ and P2P+ components exhibit self-similar behavior and can be approximated by Poisson at smaller time scales.

The ease of deployment and the self configurable aspect of wireless mesh networks has generated lot of interest in them during the recent years. The one laptop per child (OLPC) project uses mesh networking, closely adhering to IEEE 802.11s WLAN mesh standard, to provide networking in a classroom environment. Our work measures and analyzes the performance of OLPC mesh networks. It includes several experiments concerning performance of UDP and TCP across a range of network topologies. Our experiments provide useful information about several aspects of these networks, including connectivity range, effect of RTS/CTS and Tx/Rx capabilities.

In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully in a limited time. We employ the Protocol Model for successful packet reception usually adopted in network capacity studies and provide novel upper and lower bounds for the broadcast capacity for arbitrary connected networks. In a homogeneous dense network these bounds simplify to Θ(W/max(1,Δd)) where W is the wireless channel capacity, Δ the interference parameter, and d the number of dimensions of space in which the network lies. Interestingly, we show that the broadcast capacity does not change by more than a constant factor when we vary the number of nodes, the radio range, the area of the network, and even the node mobility. To address the achievability of capacity, we demonstrate that any broadcast scheme based on a backbone of size proportional to the Minimum Connected Dominating Set guarantees a throughput within a constant factor of the broadcast capacity. Finally, we demonstrate that broadcast capacity, in stark contrast to unicast capacity, does not depend on the choice of source nodes or the dimension of the network.

This paper develops a novel color-based broadcast scheme for wireless ad hoc networks where each forwarding of the broadcast message is assigned a color from a given pool of colors. A node only forwards the message if it can assign it a color from the pool which it has not already overheard after a random time. In the closely related counter-based broadcast scheme a node simply counts the number of broadcasts not the colors overheard. The forwarding nodes form a so-called backbone, which is determined by the random timers and, thus, is random itself. Notably, any counter-generated backbone could result from pruning a color-generated backbone; the typical color-generated backbone, however, exhibits a connectivity graph richer than the counter-based ones. As a particular advantage, the colors reveal simple geometric properties of the backbones which we exploit to prove that the size of both, color- and counter-generated back-bones are within a small constant factor of the optimum. We also propose two techniques, boosting and edge-growing, that improve the performance of color- and counter-based broadcast in terms of reachability and number of rebroadcasts. Experiments reveal that the powerful boosting method is considerably more effective with the color-based schemes.

We study the problem of locating in space and over time a network pathâ s tight link, that is the link with the least available bandwidth on the path. Tight link localization benefits network-aware applications, provides insight into the causes of network congestion and ways to circumvent it, and aids network operations. We present STAB, a light-weight probing tool to locate tight links. STAB combines the probing concepts of self-induced congestion, tailgating, and packet chirps in a novel fashion. We demonstrate its capabilities through experiments on the Internet and verify our results using router MRTG data.

The small-time (sub-seconds) scaling behaviors of Internet backbone traffic, based on traces collected from OC3/12/48 links in a tier-1 ISP is studied. We observe that for a majority of these traces, the (second-order) scaling exponents at small time scales (1 ms - 100 ms) are fairly close to 0.5, indicating that traffic fluctuations at these time scales are (nearly) uncorrelated. In addition, the traces manifest mostly monofractal behaviors at small time scales. The objective of the paper is to understand the potential causes or factors that influence the small-time scalings of Internet backbone traffic via empirical data analysis. We analyze the traffic composition of the traces along two dimensions - flow size and flow density. Our study uncovers dense flows (i.e., flows with bursts of densely clustered packets) as the correlation-causing factor in small time scales, and reveals that the traffic composition in terms of proportions of dense vs. sparse flows plays a major role in influencing the small-time scalings of aggregate traffic.

This paper reviews the multifractal wavelet model (MWM) and its applications to network traffic modeling and inference. The discovery of the fractal nature of traffic has made new models and analysis tools for traffic essential, since classical Poisson and Markov models do not capture important fractal properties like multiscale variability and burstiness that deleteriously affect performance. Set in the framework of multiplicative cascades, the MWM provides a link to multifractal analysis, a natural tool to characterize burstiness. The simple structure of the MWM enables fast O(N) synthesis of traffic for simulations and a tractable queuing analysis, thus rendering it suitable for real networking applications including end-to-end path modeling.

Many studies have indicated the importance of capturing scaling properties when modeling traffic loads; however, the influence of long-range dependence (LRD) and marginal statistics still remains on an unsure footing. In this paper, we study these two issues by introducing a multiscale traffic model and a novel multiscale approach to queuing analysis. The multifractal wavelet model (MWM) is a multiplicative, wavelet-based model that captures the positivity, LRD, and "spikiness" of non-Gaussian traffic. Using a binary tree, the model synthesizes an N-point data set with only O(N) computations. Leveraging the tree structure of the model, we derive a multiscale queuing analysis that provides a simple closed form approximation to the tail queue probability, valid for any given buffer size. The analysis is applicable not only to the MWM but to tree-based models in general, including fractional Gaussian noise. Simulated queuing experiments demonstrate the accuracy of the MWM for matching real data traces and the precision of our theoretical queuing formula. Thus, the MWM is useful not only for fast synthesis of data for simulation purposes but also for applications requiring accurate queuing formulas such as call admission control. Our results clearly indicate that the marginal distribution of traffic at different time-resolutions affects queuing and that a Gaussian assumption can lead to over-optimistic predictions of tail queue probability even when taking LRD into account.

In this paper, we develop a simple and powerful multiscale model for the synthesis of nonGaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have generally been restricted by a Gaussianity assumption that can be unrealistic for traffic. Using a multiplicative superstructure on top of the Haar wavelet transform, we exploit the decorrelating properties of wavelets while simultaneously capturing the positivity and "spikiness" of nonGaussian traffic. This leads to a swift O(N) algorithm for fitting and synthesizing N-point data sets. The resulting model belongs to the class of multifractal cascades, a set of processes with rich statistical properties. We elucidate our model's ability to capture the covariance structure of real data and then fit it to real traffic traces. Queueing experiments demonstrate the accuracy of the model for matching real data. Our results indicate that the nonGaussian nature of traffic has a significant effect on queuing.

We develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics.