@article{wang_wang_wang_2023, title={Remedy or Resource Drain: Modeling and Analysis of Massive Task Offloading Processes in Fog}, volume={10}, ISSN={["2327-4662"]}, DOI={10.1109/JIOT.2023.3245100}, abstractNote={Task offloading, which refers to processing (computation-intensive) data at facilitating servers, is an exemplary service that greatly benefits from the fog computing paradigm, which brings computation resources to the edge network for reduced application latency. However, the resource-consuming nature of task execution, as well as the sheer scale of IoT systems, raises an open and challenging question: whether fog is a remedy or a resource drain, considering frequent and massive offloading operations? This question is nontrivial, because participants of offloading processes, i.e., fog nodes, may have diversified technical specifications, while task generators, i.e., task nodes, may employ a variety of criteria to select offloading targets, resulting in an unmanageable space for performance evaluation. To overcome these challenges of heterogeneity, we propose a gravity model that characterizes offloading criteria with various gravity functions, in which individual/system resource consumption can be examined by the device/network effort metrics, respectively. Simulation results show that the proposed gravity model can flexibly describe different offloading schemes in terms of application and node-level behavior. We find that the expected lifetime and device effort of individual tasks decrease as $O({}{1}/{N})$ over the network size $N$ , while the network effort decreases much slower, even remain $O(1)$ when load balancing measures are employed, indicating a possible resource drain in the edge network.}, number={13}, journal={IEEE INTERNET OF THINGS JOURNAL}, author={Wang, Jie and Wang, Wenye and Wang, Cliff}, year={2023}, month={Jul}, pages={11669–11682} }
@article{wang_wang_wang_song_2022, title={Spectrum Activity Surveillance: Modeling and Analysis From Perspectives of Surveillance Coverage and Culprit Detection}, volume={21}, ISSN={["1558-0660"]}, url={https://doi.org/10.1109/TMC.2020.3032434}, DOI={10.1109/TMC.2020.3032434}, abstractNote={Spectrum activity surveillance (SAS) is essential to dynamic spectrum access (DSA)-enabled systems with a two-fold impact: it is a primitive mechanism to collect usage data for spectrum efficiency improvement; it is also a prime widget to collect misuse forensics of unauthorized or malicious users. While realizing SAS for DSA-enabled systems appears to be intuitive and trivial, it is, however, a challenging yet open problem. On one hand, a large-scale SAS function is costly to implement in practice; on the other hand, it is not clear how to characterize the efficacy and performance of monitor deployment strategies. To address such challenges, we introduce a three-factor space, composed of spectrum, time, and geographic region, over which the SAS problem is formulated by a two-step solution: 3D-tessellation for sweep (monitoring) coverage and graph walk for detecting spectrum culprits, that is, devices responsible for unauthorized spectrum occupancy. In particular, our system model transforms SAS from a globally collective activity to localized (even distributed) actions, and strategy objectives from qualitative attributes to quantitative measures. With this model, we design low-cost deterministic strategies for dedicated monitors, which outperform strategies found by genetic algorithms, and performance-guaranteed random strategies for crowd-source monitors, which can detect adversarial spectrum culprits in bounded time.}, number={5}, journal={IEEE TRANSACTIONS ON MOBILE COMPUTING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Wang, Jie and Wang, Wenye and Wang, Cliff and Song, Min}, year={2022}, month={May}, pages={1829–1846} }
@article{liu_wang_jiang_2021, title={A phase-field simulation-based approach to determine flexoelectric coefficients from hysteresis loop of ferroelectrics}, volume={130}, ISSN={["1089-7550"]}, DOI={10.1063/5.0055511}, abstractNote={The flexoelectric effects in ferroelectric materials have attracted abundant attention in recent years due to the potential application in microscale mechanical-electrical devices. However, quantifying flexoelectric coefficients in ferroelectrics is still a challenge because of the complex electromechanical coupling in ferroelectric materials. Inspired by the flexoelectric effect-induced skew of the hysteresis curve, an indirect method is proposed in this article to determine the flexoelectric coefficient with reasonably high accuracy. Phase-field simulations show that such skew is stimulated only by the flexoelectric effect, which excludes the influence of other electromechanical coupling effects. In addition, the magnitude of such skew is in linear proportion to the flexoelectric coefficient. The four-point bending PbTiO3 beam model is calculated as an example to obtain the transversal flexoelectric coefficient of f1122.}, number={14}, journal={JOURNAL OF APPLIED PHYSICS}, author={Liu, Chang and Wang, Jie and Jiang, Xiaoning}, year={2021}, month={Oct} }
@article{hosseinalipour_wang_tian_dai_2020, title={Infection Analysis on Irregular Networks Through Graph Signal Processing}, url={https://doi.org/10.1109/TNSE.2019.2958892}, DOI={10.1109/TNSE.2019.2958892}, abstractNote={In a networked system, functionality can be seriously endangered when nodes are infected, due to internal random failures or a contagious virus that develops into an epidemic. Given a snapshot of the network representing the nodes' states (infected or healthy), infection analysis refers to distinguishing an epidemic from random failures and gathering information for effective countermeasure design. This analysis is challenging due to irregular network structure, heterogeneous epidemic spreading, and noisy observations. This paper treats a network snapshot as a graph signal, and develops effective approaches for infection analysis based on graph signal processing. For the macro (network-level) analysis aiming to distinguish an epidemic from random failures, 1) multiple detection metrics are defined based on the graph Fourier transform (GFT) and neighborhood characteristics of the graph signal; 2) a new class of graph wavelets, distance-based graph wavelets (DBGWs), are developed; and 3) a machine learning-based framework is designed employing either the GFT spectrum or the graph wavelet coefficients as features for infection analysis. DBGWs also enable the micro (node-level) infection analysis, through which the performance of epidemic countermeasures can be improved. Extensive simulations are conducted to demonstrate the effectiveness of all the proposed algorithms in various network settings.}, journal={IEEE Transactions on Network Science and Engineering}, author={Hosseinalipour, Seyyedali and Wang, Jie and Tian, Yuanzhe and Dai, Huaiyu}, year={2020}, month={Jul} }
@article{wang_wang_wang_2020, title={Modeling and Analysis of Conflicting Information Propagation in a Finite Time Horizon}, volume={28}, url={https://doi.org/10.1109/TNET.2020.2976972}, DOI={10.1109/TNET.2020.2976972}, abstractNote={Emerging mobile applications enable people to connect with one another more easily than ever, which causes networked systems, e.g., online social networks (OSN) and Internet-of-Things (IoT), to grow rapidly in size, and become more complex in structure. In these systems, different, even conflicting information, e.g., rumor v.s. truth, and malware v.s. security patches, can compete with each other during their propagation over individual connections. For such information pairs, in which a desired information kills its undesired counterpart on contact, an interesting yet challenging question is when and how fast the undesired information dies out. To answer this question, we propose a Susceptible-Infectious-Cured (SIC) propagation model, which captures short-term competitions between the two pieces of information, and define extinction time and half-life time, as two pivots in time, to quantify the dying speed of the undesired information. Our analysis revealed the impact of network topology and initial conditions on the lifetime of the undesired information. In particular, we find that, the Cheeger constant that measures the edge expansion property of a network steers the scaling law of the lifetime with respect to the network size, and the vertex eccentricities that are easier to compute provide accurate estimation of the lifetime. Our analysis also sheds light on where to inject the desired information, such that its undesired counterpart can be eliminated faster.}, number={3}, journal={IEEE/ACM Transactions on Networking}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Wang, Jie and Wang, Wenye and Wang, Cliff}, year={2020}, month={Jun}, pages={972–985} }
@article{wang_pambudi_wang_song_2019, title={Resilience of IoT Systems Against Edge-Induced Cascade-of-Failures: A Networking Perspective}, volume={6}, ISSN={["2327-4662"]}, url={https://doi.org/10.1109/JIOT.2019.2913140}, DOI={10.1109/JIOT.2019.2913140}, abstractNote={Internet of Things (IoT) is a networking paradigm that interconnects physical systems to the cyber world, to provide automation and intelligence via interdependent links between the two domains. Such interdependence renders IoT systems vulnerable to random failures, e.g., broken communication links or crashed cyber instances, because a single incident in one domain can develop into a cascade-of-failures across domains, which dissolves the network structure, and has devastating consequences. To answer how robust an IoT system is, this paper studies its resilience by examining the impact of edge- and jointly-induced cascades, that is, a sequence of failures caused by randomly broken physical links (and simultaneous failing cyber nodes). Resilience of an IoT system is quantified by two new metrics, the critical edge disconnecting probability φ _{cr} , i.e., the maximum intensity of random failures the system can withstand, and the cascade length τ _{cf} , i.e., the lifetime of a cascade. For IoT systems with Poisson degree distributions, we derive exact solutions for the critical disconnecting probability φ _{cr} , above which an edge-induced cascade will completely fragment the network. We also find that the critical condition φ _{cr} marks a dichotomy of the expected cascade length E(τ _{cf} ): for the super-critical (φ > φ _{cr} ) scenario, we obtain E(τ _{cf} ) ~ exp(1 - φ) through analysis, while for the subcritical scenario, we observe E(τ _{cf} ) ~ exp(1/1 - φ) through simulations. With these results, the final outcome of a cascade can be anticipated upon the initial failures, while the reaction window of time-sensitive countermeasures can be obtained before a cascade fully unfolds.}, number={4}, journal={IEEE INTERNET OF THINGS JOURNAL}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Wang, Jie and Pambudi, Sigit and Wang, Wenye and Song, Min}, year={2019}, month={Aug}, pages={6952–6963} }
@inproceedings{hosseinalipour_wang_dai_wang_2017, title={Detection of infections using graph signal processing in heterogeneous networks}, DOI={10.1109/glocom.2017.8254487}, abstractNote={Determining the causality of abnormalities in a network is the prerequisite for developing countermeasures. In this paper, we focus on infection detection in heterogeneous networks. Given a snapshot of the network which demonstrates the condition of the nodes, the goal is to distinguish between random failures and epidemic scenarios. We model the network situation as a graph signal based on the nodes' status. Detection metrics motivated by graph signal processing are introduced for the infection detection problem in hand, and an effective algorithm is proposed to solve it. Simulation results indicate a dramatic improvement in terms of detection probability compared to the current state-of-the-art.}, booktitle={Globecom 2017 - 2017 ieee global communications conference}, author={Hosseinalipour, S. and Wang, Jie and Dai, Huaiyu and Wang, Wenye}, year={2017} }
@inproceedings{wang_wang_wang_2017, title={Modeling and strategy design for spectrum monitoring over a geographical region}, DOI={10.1109/glocom.2017.8254113}, abstractNote={Spectrum monitoring is a prerequisite in dynamic access regulation, policy enforcement, as well as spectrum database establishment. In this paper, we introduce the dimension of geographical space into the spectrum monitoring problem, and studied deployment strategies of multiple monitors, in terms of coverage time and cost. The monitoring problem is modeled as a 3-d continuous sweep coverage problem, whose solution space is then reduced by effectively dividing the spectra-location space, in order to achieve a small coverage time. The cost minimization is then formulated as a Multiple Traveling Salesman problem (MTSP), which is NP-hard. By observing the structure of the strategy space, we propose a solution that attains a reasonable cost, without applying complex optimization algorithms.}, booktitle={Globecom 2017 - 2017 ieee global communications conference}, author={Wang, Jie and Wang, Wenye and Wang, C.}, year={2017} }
@article{wang_wang_wang_2016, title={Divide and Conquer: Leveraging Topology in Control of Epidemic Information Dynamics}, ISSN={["2576-6813"]}, DOI={10.1109/glocom.2016.7841747}, abstractNote={As online social networks grow in both size and connectivity, epidemic information dynamics in such networks is attracting considerable research interests, due to its impact on both the network and individuals. This paper studies control of malicious information (virus) epidemic with replicable antidote information, taking topological characteristics of the underlying graph into consideration. Specifically, we analytically relate the extinction time of the virus to the diameter and giant component size of the remaining graph after the initial antidote distribution. With this 'divide and conquer' guideline, topology-based antidote distribution approaches are designed, and then examined through simulations in real world network portions.}, journal={2016 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={Wang, Jie and Wang, Wenye and Wang, Cliff}, year={2016} }
@article{wang_wang_wang_2016, title={How the Anti-Rumor Kills the Rumor: Conflicting Information Propagation in Networks}, ISSN={["1550-3607"]}, DOI={10.1109/icc.2016.7511492}, abstractNote={Online Social Networks (OSNs) is taking over television and newspapers, to be the dominant information dissemination option. The growing involvement of individuals create the situation that colliding, even contradicting information coexist and propagate in the same network, which gives rise to an interesting question: how will the conflicting information propagate? To answer this question, the propagation process is described to be an Susceptible-Infected-Cured (SIC) epidemic, and we propose an inference algorithm to study the transient behavior of the competing propagation processes in connected networks. Moreover, we provide an analytic method to derive the conditional infection count distribution for networks with special topologies, as a step further to understand the evolution. A trace collected from the Internet is analyzed to validate our model and methods.}, journal={2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)}, author={Wang, Jie and Wang, Wenye and Wang, Cliff}, year={2016} }
@inproceedings{wang_wang_2016, title={To live or to die: Encountering conflict information dissemination over simple networks}, DOI={10.1109/infocom.2016.7524603}, abstractNote={In an era of networks in which any individual is connected with one another, such as Internet of Things (IoT) and Online Social Networks (OSNs), the networks are evolving into complex systems, carrying a huge volume of information that may provoke even more. An interesting, yet challenging question is how such information dissemination evolves, that is, to continue or to stop. Specifically, we aim to find out the aftermath of epidemic spreading via individuals and conflicting information dissemination. From a holistic, networking view, it is impossible to take every aspect into accounts for complex networks toward these questions. Therefore, we establish a Susceptible-Infectious-Cured (SIC) propagation model to examine two simple network topologies, clique and star, in terms of extinction time and half-life time of information under controllable, epidemic dynamics. For a network of size n, both theoretical and numerical results suggest that extinction time and half-life time are O(log n/n) for clique networks, and O(log n) for star networks. More interestingly, given an initial network state I0, the extinction time is constant (O(1)) for cliques, and O(log I0) for stars; while the half-life time is O(log 1/I0) for both clique and star networks, respectively. In addition, we developed a method to estimate the conditional infection count distribution, which indicates the scope of information dissemination.}, booktitle={IEEE INFOCOM 2016 - the 35th annual IEEE international Conference on Computer Communications}, author={Wang, Jie and Wang, Wenye}, year={2016} }