arXiv subjects

Raja Jurdak

Publications and source records attributed to Raja Jurdak.

At least 109 records · Page 6Linked to original sources

arXiv2017

Multi-mode Tracking of a Group of Mobile Agents

We consider the problem of tracking a group of mobile nodes with limited available computational and energy resources given noisy RSSI measurements and position estimates from group members. The multilateration solutions are known for energy efficiency. However, these solutions are not directly applicable to dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. Existing algorithms such as cluster-based GPS duty-cycling, individual-based tracking, and multilateration-based tracking can only partially deal with the challenges of dynamic grouping scenarios. To cope with these challenges in an effective manner, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the topological structure of the group as well as the availability of the resources into consideration and decides the best solution at any particular time instance. We consider a clustering approach where a cluster head coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes generated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to $20\%$ in comparison to the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using almost half the energy.

cs.IT↗

arXiv2017

Modeling Reflexivity of Social Systems in Disease Spread

Diffusion processes in a social system are governed by external triggers and internal excitations via interactions between individuals over social networks. Underlying mechanisms are crucial to understand emergent phenomena in the real world and accordingly establish effective strategies. However, it is challenging to reveal the dynamics of a target diffusion process due to invisible causality between events and their time-evolving intensity. In this study, we propose the Latent Influence Point Process model (LIPP) by incorporating external heterogeneity and internal dynamics of meta-populations based on human mobility. Our proposed model quantifies the reflexivity of a social system, which is the level of feedback on event occurrences by its internal dynamics. As an exemplary case study, we investigate dengue outbreaks in Queensland, Australia during the last 15 years. We find that abrupt and steady growth of disease outbreaks relate to exogenous and endogenous influences respectively. Similar diffusion trends between regions reflect synchronous reflexivity of a regional social system, likely driven by human mobility.

cs.SI↗

arXiv2017

RSSI-Based Self-Localization with Perturbed Anchor Positions

We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.

cs.IT↗

arXiv2017

BlockChain: A distributed solution to automotive security and privacy

Interconnected smart vehicles offer a range of sophisticated services that benefit the vehicle owners, transport authorities, car manufacturers and other service providers. This potentially exposes smart vehicles to a range of security and privacy threats such as location tracking or remote hijacking of the vehicle. In this article, we argue that BlockChain (BC), a disruptive technology that has found many applications from cryptocurrencies to smart contracts, is a potential solution to these challenges. We propose a BC-based architecture to protect the privacy of the users and to increase the security of the vehicular ecosystem. Wireless remote software updates and other emerging services such as dynamic vehicle insurance fees, are used to illustrate the efficacy of the proposed security architecture. We also qualitatively argue the resilience of the architecture against common security attacks.

cs.CR↗

arXiv2017

Predictability of Irregular Human Mobility

Understanding human mobility is critical for decision support in areas from urban planning to infectious diseases control. Prior work has focused on tracking daily logs of outdoor mobility without considering relevant context, which contain a mixture of regular and irregular human movement for a range of purposes, and thus diverse effects on the dynamics have been ignored. This study aims to focus on irregular human movement of different meta-populations with various purposes. We propose approaches to estimate the predictability of mobility in different contexts. With our survey data from international and domestic visitors to Australia, we found that the travel patterns of Europeans visiting for holidays are less predictable than those visiting for education, while East Asian visitors show the opposite patterns, ie, more predictable for holidays than for education. Domestic residents from the most populous Australian states exhibit the most unpredictable patterns, while visitors from less populated states show the highest predictable movement.

physics.soc-ph↗

arXiv2017

Cluster-based Position Tracking of Mobile Sensors

Tracking movement of mobile nodes has received significant scientific and commercial interest, but long term tracking of resource-constrained mobile nodes remains challenging due to the high energy consumption of satellite receivers. Cooperative position tracking has been proposed for energy efficiency, however, all the cooperative schemes use opportunistic cooperation and optimize for either energy or accuracy. Considering the existence of a reasonably stable group of mobile nodes like animals, birds, and mobile assets, we propose a cluster-based cooperative tracking algorithm, where cluster head centrally coordinates resource usage among cluster members. Variants of this strategy include the use of a cooperative Kalman filter with and without inertial sensor inputs to estimate nodes positions. We use the Boid flocking algorithm to generate group position movements in 3D and perform various experiments to compare the energy and position accuracy tradeoff of our proposed scheme with individual-based tracking and existing cooperative schemes. We perform the experiments for fixed periodic GPS sampling and dynamic GPS sampling triggered by node position error uncertainty tolerance limit. Experiments results show that in periodic sampling scheme cooperative tracking yields more than one-quarter reduction in energy consumption and more than one-third improvement in position accuracy over individual-based tracking, however, results for dynamic sampling scheme are comparable with existing cooperative scheme.

cs.NI↗

arXiv2017

Universal Components of Real-world Diffusion Dynamics based on Point Processes

Bursts in human and natural activities are highly clustered in time, suggesting that these activities are influenced by previous events within the social or natural system. Bursty behavior in the real world conveys information of underlying diffusion processes, which have been the focus of diverse scientific communities from online social media to criminology and epidemiology. However, universal components of real-world diffusion dynamics that cut across disciplines remain unexplored. Here, we introduce a wide range of diffusion processes across disciplines and propose universal components of diffusion frameworks. We apply these components to diffusion-based studies of human disease spread, through a case study of the vector-borne disease dengue. The proposed universality of diffusion can motivate transdisciplinary research and provide a fundamental framework for diffusion models.

cs.SI↗

arXiv2017

WLS-Based Self-Localization Using Perturbed Anchor Positions and RSSI Measurements

We consider the problem of self-localization by a resource-constrained node within a network given radio signal strength indicator (RSSI) measurements from a set of anchor nodes where the RSSI measurements as well as the anchor position information are subject to perturbation. In order to achieve a computationally efficient estimate for the unknown position, we minimize a weighted sum-square-distance-error cost function in an iterative fashion utilizing the gradient-descent method. We calculate the weights in the cost function by taking into account perturbations in both RSSI measurements and anchor node position information while assuming normal distribution for the perturbations in the anchor node position information and log-normal distribution for the RSSI-induced distance estimates. The latter assumption is due to considering the log-distance path-loss model with normally-distributed perturbations for the RSSI measurements in the logarithmic scale. We also derive the Cramer-Rao lower bound associated with the considered position estimation problem. We evaluate the performance of the proposed algorithm considering various arbitrary network topologies and compare it with an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. The experimental results show that the proposed algorithm yields significant improvement in localization performance over the existing algorithm while maintaining its computational efficiency. This makes the proposed algorithm suitable for real-world applications where the information available about the positions of anchor nodes often suffer from uncertainty due to observational noise or error and the computational and energy resources of mobile nodes are limited, prohibiting the use of more sophisticated techniques such as those based on semidefinite or second-order cone programming.

cs.IT↗

arXiv2016

Blockchain in internet of things: Challenges and Solutions

The Internet of Things IoT is experiencing exponential growth in research and industry, but it still suffers from privacy and security vulnerabilities. Conventional security and privacy approaches tend to be inapplicable for IoT, mainly due to its decentralized topology and the resource-constraints of the majority of its devices. BlockChain BC that underpin the crypto-currency Bitcoin have been recently used to provide security and privacy in peer-to-peer networks with similar topologies to IoT. However, BCs are computationally expensive and involve high bandwidth overhead and delays, which are not suitable for IoT devices. This position paper proposes a new secure, private, and lightweight architecture for IoT, based on BC technology that eliminates the overhead of BC while maintaining most of its security and privacy benefits. The described method is investigated on a smart home application as a representative case study for broader IoT applications. The proposed architecture is hierarchical, and consists of smart homes, an overlay network and cloud storages coordinating data transactions with BC to provide privacy and security. Our design uses different types of BCs depending on where in the network hierarchy a transaction occurs, and uses distributed trust methods to ensure a decentralized topology. Qualitative evaluation of the architecture under common threat models highlights its effectiveness in providing security and privacy for IoT applications.

cs.CR↗

arXiv2016

A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments

State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45%). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.

cs.DS↗

arXiv2016

Understanding the spatiotemporal pattern of grazing cattle movement

In this study, we analyse a high-frequency movement dataset for a group of grazing cattle and investigate their spatiotemporal patterns using a simple two-state `stop-and-move' mobility model. We find that the dispersal kernel in the moving state is best described by a mixture exponential distribution, indicating the hierarchical nature of the movement. On the other hand, the waiting time appears to be scale-invariant below a certain cut-off and is best described by a truncated power-law distribution, suggesting heterogenous dynamics in the non-moving state. We explore possible explanations for the observed phenomena, covering factors that can play a role in the generation of mobility patterns, such as the context of grazing environment, the intrinsic decision-making mechanism or the energy status of different activities. In particular, we propose a new hypothesis that the underlying movement pattern can be attributed to the most probable observable energy status under the maximum entropy configuration. These results are not only valuable for modelling cattle movement but also provide new insights for understanding the underlying biological basis of grazing behaviour.

q-bio.QM↗

arXiv2016

Fair Scheduling for Data Collection in Mobile Sensor Networks with Energy Harvesting

We consider the problem of data collection from a continental-scale network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favour a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimisation problem for energy harvesting mobile sensor network, that maximises the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-the-art. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

cs.NI↗

arXiv2015

Diffusion in Colocation Contact Networks: the Impact of Nodal Spatiotemporal Dynamics

Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation events giving rise to contact networks with varying spreading potential. This enables second-order causal reasoning about how correlations in nodes' spatiotemporal preferences not only lead to a given contact network but ultimately influence the network's spreading potential. We find the correlation between nodes and times to be the greatest impediment to spreading, while the correlation between times and locations slightly catalyzes spreading. Under each of the presented null models we measure both the number of contacts and infection prevalence as a function of time, with the surprising finding that the two have no direct causality.

cs.SI↗

arXiv2015

Opportunistic and Context-aware Affect Sensing on Smartphones: The Concept, Challenges and Opportunities

Opportunistic affect sensing offers unprecedented potential for capturing spontaneous affect ubiquitously, obviating biases inherent in the laboratory setting. Facial expression and voice are two major affective displays, however most affect sensing systems on smartphone avoid them due to extensive power requirement. Encouragingly, due to the recent advent of low-power DSP (Digital Signal Processing) co-processor and GPU (Graphics Processing Unit) technology, audio and video sensing are becoming more feasible. To properly evaluate opportunistically captured facial expression and voice, contextual information about the dynamic audio-visual stimuli needs to be inferred. This paper discusses recent advances of affect sensing on the smartphone and identifies the key barriers and potential solutions of implementing opportunistic and context-aware affect sensing on smartphone platforms.

cs.HC↗

arXiv2015

Delay-Tolerant Networking for Long-Term Animal Tracking

Enabling Internet connectivity for mobile objects that do not have a permanent home or regular movements is a challenge due to their varying energy budget, intermittent wireless connectivity, and inaccessibility. We present a hardware and software framework that offers robust data collection, adaptive execution of sensing tasks, and flexible remote reconfiguration of devices deployed on nomadic mobile objects such as animals. The framework addresses the overall complexity through a multi-tier architecture with low tier devices operating on a tight energy harvesting budget and high tier cloud services offering seamless delay-tolerant presentation of data to end users. Based on our multi-year experience of applying this framework to animal tracking and monitoring applications, we present the main challenges that we have encountered, the design of software building blocks that address these challenges, and examples of the data we collected on flying foxes.

cs.NI↗

arXiv2015

Affect Sensing on Smartphone - Possibilities of Understanding Cognitive Decline in Aging Population

Due to increasing sensing capacity, smartphones offer unprecedented opportunity to monitor human health. Affect sensing is one such essential monitoring that can be achieved on smartphones. Information about affect can be useful for many modern applications. In particular, it can be potentially used for understanding cognitive decline in aging population. In this paper we present an overview of the existing literature that offer affect sensing on smartphone platform. Most importantly, we present the challenges that need to be addressed to make affect sensing on smartphone a reality.

cs.CY↗

arXiv2015

Understanding Human Mobility from Twitter

Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.

physics.soc-ph↗

arXiv2015

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.

cs.RO↗