Stochastic Petri Nets for Wireless Networks

This SpringerBrief presents research in the application of Stochastic Petri Nets (SPN) to the performance evaluation of wireless networks under bursty traffic. It covers typical Quality-of-Service performance metrics such as mean throughput, average delay and packet dropping probability. Along with an introduction of SPN basics, the authors introduce the key motivation and challenges of using SPN to analyze the resource sharing performance in wireless networks. The authors explain two powerful modeling techniques that treat the well-known state space explosion problem: model decomposition and iteration, and model aggregation using stochastic high-level petri nets. The first technique assists in performance analysis of opportunistic scheduling, Device-to-Device communications with full frequency reuse and partial frequency reuse. The second technique is used to formulate a wireless channel mode for cross-layer performance analysis in OFDM system. Stochastic Petri Nets for Wireless Networks reveals useful insights for the design of radio resource management algorithms and a new line of thinking for the performance evaluation of future wireless networks. This material is valuable as a reference for researchers and professionals working in wireless networks and for advanced-level students studying wireless technologies in electrical engineering or computer science

Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

[EN] One of the three main use cases of the fifth generation of mobile networks (5G) is massive machine-type communications (mMTC). The latter refers to the highly synchronized accesses to the cellular base stations from a great number of wireless devices, as a product of the automated exchange of small amounts of data. Clearly, an efficient mMTC is required to support the Internet-of-Things (IoT). Nevertheless, the method to change from idle to connected mode, known as the random access procedure (RAP), of 4G has been directly inherited by 5G, at least, until the first phase of standardization. Research has demonstrated the RAP is inefficient to support mMTC, hence, access control schemes are needed to obtain an adequate performance. In this paper, we compare the benefits of using different filtering methods to configure an access control scheme included in the 5G standards: the access class barring (ACB), according to the intensity of access requests. These filtering methods are a key component of our proposed ACB configuration scheme, which can lead to more than a three-fold increase in the probability of successfully completing the random access procedure under the most typical network configuration and mMTC scenario.This research has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R and Grant TEC2015-71932-REDT. The research of I. Leyva-Mayorga was partially funded by grant 383936 CONACYT-GEM 2014.Leyva-Mayorga, I.; Rodríguez-Hernández, MA.; Pla, V.; Martínez Bauset, J. (2019). Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios. Electronics. 8(1):1-18. https://doi.org/10.3390/electronics8010027S11881Laya, A., Alonso, L., & Alonso-Zarate, J. (2014). Is the Random Access Channel of LTE and LTE-A Suitable for M2M Communications? A Survey of Alternatives. IEEE Communications Surveys & Tutorials, 16(1), 4-16. doi:10.1109/surv.2013.111313.00244Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks, 1(1), 1-19. doi:10.1016/j.dcan.2015.02.001Tello-Oquendo, L., Leyva-Mayorga, I., Pla, V., Martinez-Bauset, J., Vidal, J.-R., Casares-Giner, V., & Guijarro, L. (2018). Performance Analysis and Optimal Access Class Barring Parameter Configuration in LTE-A Networks With Massive M2M Traffic. IEEE Transactions on Vehicular Technology, 67(4), 3505-3520. doi:10.1109/tvt.2017.2776868Tavana, M., Rahmati, A., & Shah-Mansouri, V. (2018). Congestion control with adaptive access class barring for LTE M2M overload using Kalman filters. Computer Networks, 141, 222-233. doi:10.1016/j.comnet.2018.01.044Lin, T.-M., Lee, C.-H., Cheng, J.-P., & Chen, W.-T. (2014). PRADA: Prioritized Random Access With Dynamic Access Barring for MTC in 3GPP LTE-A Networks. IEEE Transactions on Vehicular Technology, 63(5), 2467-2472. doi:10.1109/tvt.2013.2290128De Andrade, T. P. C., Astudillo, C. A., Sekijima, L. R., & Da Fonseca, N. L. S. (2017). The Random Access Procedure in Long Term Evolution Networks for the Internet of Things. IEEE Communications Magazine, 55(3), 124-131. doi:10.1109/mcom.2017.1600555cmWang, Z., & Wong, V. W. S. (2015). Optimal Access Class Barring for Stationary Machine Type Communication Devices With Timing Advance Information. IEEE Transactions on Wireless Communications, 14(10), 5374-5387. doi:10.1109/twc.2015.2437872Tello-Oquendo, L., Pacheco-Paramo, D., Pla, V., & Martinez-Bauset, J. (2018). Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications. 2018 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2018.8422167Leyva-Mayorga, I., Rodriguez-Hernandez, M. A., Pla, V., Martinez-Bauset, J., & Tello-Oquendo, L. (2019). Adaptive access class barring for efficient mMTC. Computer Networks, 149, 252-264. doi:10.1016/j.comnet.2018.12.003Kalalas, C., & Alonso-Zarate, J. (2017). Reliability analysis of the random access channel of LTE with access class barring for smart grid monitoring traffic. 2017 IEEE International Conference on Communications Workshops (ICC Workshops). doi:10.1109/iccw.2017.7962744Leyva-Mayorga, I., Tello-Oquendo, L., Pla, V., Martinez-Bauset, J., & Casares-Giner, V. (2016). Performance analysis of access class barring for handling massive M2M traffic in LTE-A networks. 2016 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2016.7510814Arouk, O., & Ksentini, A. (2016). General Model for RACH Procedure Performance Analysis. IEEE Communications Letters, 20(2), 372-375. doi:10.1109/lcomm.2015.2505280Zhang, Z., Chao, H., Wang, W., & Li, X. (2014). Performance Analysis and UE-Side Improvement of Extended Access Barring for Machine Type Communications in LTE. 2014 IEEE 79th Vehicular Technology Conference (VTC Spring). doi:10.1109/vtcspring.2014.7023042Cheng, R.-G., Chen, J., Chen, D.-W., & Wei, C.-H. (2015). Modeling and Analysis of an Extended Access Barring Algorithm for Machine-Type Communications in LTE-A Networks. IEEE Transactions on Wireless Communications, 14(6), 2956-2968. doi:10.1109/twc.2015.2398858Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., … Goodlin, R. C. (1975). Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE, 63(12), 1692-1716. doi:10.1109/proc.1975.1003

Modeling of Duty-Cycled MAC Protocols for Heterogeneous WSN with Priorities

[EN] Wireless Sensor Networks (WSN) have experienced an important revitalization, particularly with the arrival of Internet of Things applications. In a general sense, a WSN can be composed of different classes of nodes, having different characteristics or requirements (heterogeneity). Duty-cycling is a popular technique used in WSN, that allows nodes to sleep and wake up periodically in order to save energy. We believe that the modeling and performance evaluation of heterogeneous WSN with priorities operating in duty-cycling, being of capital importance for their correct design and successful deployment, have not been sufficiently explored. The present work presents a performance evaluation study of a WSN with these features. For a scenario with two classes of nodes composing the network, each with a different channel access priority, an approximate analytical model is developed with a pair of two-dimensional discrete-time Markov chains. Note that the same modeling approach can be used to analyze networks with a larger number of classes. Performance parameters such as average packet delay, throughput and average energy consumption are obtained. Analytical results are validated by simulation, showing accurate results. Furthermore, a new procedure to determine the energy consumption of nodes is proposed that significantly improves the accuracy of previous proposals. We provide quantitative evidence showing that the energy consumption accuracy improvement can be up to two orders of magnitudeThis work is part of the project PGC2018-094151-B-I00, which is financed by the Ministerio de Ciencia, Innovacion y Universidades (MCIU), Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) (MCIU/AEI/FEDER.UE). C. Portillo acknowledges the funding received from the European Union under the program Erasmus Mundus Partnerships, project EuroinkaNet, GRANT AGREEMENT NUMBER -2014 -0870/001/001, and the support received from SEP-SES (DSA/103.5/15/6629)Portillo, C.; Martínez Bauset, J.; Pla, V.; Casares-Giner, V. (2020). Modeling of Duty-Cycled MAC Protocols for Heterogeneous WSN with Priorities. Electronics. 9(3):1-16. https://doi.org/10.3390/electronics9030467S11693Gomes, D. A., & Bianchini, D. (2016). Interconnecting Wireless Sensor Networks with the Internet Using Web Services. IEEE Latin America Transactions, 14(4), 1937-1942. doi:10.1109/tla.2016.7483537Libo, Z., Tian, H., & Chunyun, G. (2019). Wireless multimedia sensor network for rape disease detections. EURASIP Journal on Wireless Communications and Networking, 2019(1). doi:10.1186/s13638-019-1468-3Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Dai, H.-N., Ng, K.-W., & Wu, M.-Y. (2013). On Busy-Tone Based MAC Protocol for Wireless Networks with Directional Antennas. Wireless Personal Communications, 73(3), 611-636. doi:10.1007/s11277-013-1206-9Padilla, P., Padilla, J. L., Valenzuela-Valdés, J. F., Serrán-González, J.-V., & López-Gordo, M. A. (2015). Performance Analysis of Different Link Layer Protocols in Wireless Sensor Networks (WSN). Wireless Personal Communications, 84(4), 3075-3089. doi:10.1007/s11277-015-2783-6Ye, W., Heidemann, J., & Estrin, D. (2004). Medium Access Control With Coordinated Adaptive Sleeping for Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 12(3), 493-506. doi:10.1109/tnet.2004.828953Kuo, Y.-W., Li, C.-L., Jhang, J.-H., & Lin, S. (2018). Design of a Wireless Sensor Network-Based IoT Platform for Wide Area and Heterogeneous Applications. IEEE Sensors Journal, 18(12), 5187-5197. doi:10.1109/jsen.2018.2832664He, X., Liu, S., Yang, G., & Xiong, N. (2018). Achieving Efficient Data Collection in Heterogeneous Sensing WSNs. IEEE Access, 6, 63187-63199. doi:10.1109/access.2018.2876552Ortin, J., Cesana, M., Redondi, A. E. C., Canales, M., & Gallego, J. R. (2019). Analysis of Unslotted IEEE 802.15.4 Networks With Heterogeneous Traffic Classes. IEEE Wireless Communications Letters, 8(2), 380-383. doi:10.1109/lwc.2018.2873347Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210Liu, R. P., Sutton, G. J., & Collings, I. B. (2010). A New Queueing Model for QoS Analysis of IEEE 802.11 DCF with Finite Buffer and Load. IEEE Transactions on Wireless Communications, 9(8), 2664-2675. doi:10.1109/twc.2010.061010.091803Ou Yang, & Heinzelman, W. (2012). Modeling and Performance Analysis for Duty-Cycled MAC Protocols with Applications to S-MAC and X-MAC. IEEE Transactions on Mobile Computing, 11(6), 905-921. doi:10.1109/tmc.2011.121Martinez-Bauset, J., Guntupalli, L., & Li, F. Y. (2015). Performance Analysis of Synchronous Duty-Cycled MAC Protocols. IEEE Wireless Communications Letters, 4(5), 469-472. doi:10.1109/lwc.2015.2439267Guntupalli, L., Martinez-Bauset, J., Li, F. Y., & Weitnauer, M. A. (2017). Aggregated Packet Transmission in Duty-Cycled WSNs: Modeling and Performance Evaluation. IEEE Transactions on Vehicular Technology, 66(1), 563-579. doi:10.1109/tvt.2016.2536686Zhang, R., Moungla, H., Yu, J., & Mehaoua, A. (2017). Medium Access for Concurrent Traffic in Wireless Body Area Networks: Protocol Design and Analysis. IEEE Transactions on Vehicular Technology, 66(3), 2586-2599. doi:10.1109/tvt.2016.2573718Guntupalli, L., Martinez-Bauset, J., & Li, F. Y. (2018). Performance of frame transmissions and event-triggered sleeping in duty-cycled WSNs with error-prone wireless links. Computer Networks, 134, 215-227. doi:10.1016/j.comnet.2018.01.047(July, 2019). The State Transition Probabilities of the Two 2D-DTMC. Technical Report http://personales.upv.es/jmartine/public/2DDTMC.pdfCrossbow Technology Incorporated, San Jose, CA, USA http://www.openautomation.net/uploadsproductos/micaz-datasheet.pd

Performance model for two-tier mobile wireless networks with macrocells and small cells

[EN] A new analytical model is proposed to evaluate the performance of two-tier cellular networks composed of macrocells (MCs) and small cells (SCs), where terminals roam across the service area. Calls being serviced by MCs may retain their channel when entering a SC service area, if no free SC channels are available. Also, newly offered SC calls can overflow to the MC. However, in both situations channels may be repacked to vacate MC channels. The cardinality of the state space of the continuous-time Markov chain (CTMC) that models the system dynamics makes the exact system analysis unfeasible. We propose an approximation based on constructing an equivalent CTMC for which a product-form solution exist that can be obtained with very low computational complexity. We determine performance parameters such as the call blocking probabilities for the MC and SCs, the probability of forced termination, and the carried traffic. We validate the analytical model by simulation. Numerical results show that the proposed analytical model achieves very good precision in scenarios with diverse mobility rates and MCs and SCs loads, as well as when MCs overlay a large number of SCs.Authors would like to thank you the anonymous reviewers for the review comments provided to our work that have decisively contributed to improve the paper. Most of the contribution of V. Casares-Giner was done while visiting the Huazhong University of Science and Technolgy (HUST), Whuhan, China. This visit was supported by the European Commission, 7FP, S2EuNet project. The authors from the Universitat Politecnica de Valencia are partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The research of Xiaohu Ge was supported by the National Natural Science Foundation of China (NSFC) grant 61210002, the Fundamental Research Funds for the Central Universities grant 2015XJGH011, and China International Joint Research Center of Green Communications and Networking grant 2015B01008.Casares-Giner, V.; Martínez Bauset, J.; Ge, X. (2018). Performance model for two-tier mobile wireless networks with macrocells and small cells. Wireless Networks. 24(4):1327-1342. https://doi.org/10.1007/s11276-016-1407-8S13271342244ABIresearch. (2016). In-building mobile data traffic forecast. ABIreseach, Technical Report.NGMN Alliance. (2015). Recommendations for small cell development and deployment. NGMN Alliance, Technical Report.Chandrasekhar, V., Andrews, J., & Gatherer, A. (2008). Femtocell networks: A survey. IEEE Communications Magazine, 46(9), 59–67.Yamamoto, T., & Konishi, S. (2013). Impact of small cell deployments on mobility performance in LTE-Advanced systems. In IEEE PIMRC workshops (pp. 189–193).Balakrishnan, R., & Akyildiz, I. (2016). Local anchor schemes for seamless and low-cost handover in coordinated small cells. IEEE Transactions on Mobile Computing, 15(5), 1182–1196.Zahir, T., Arshad, K., Nakata, A., & Moessner, K. (2013). Interference management in femtocells. IEEE Communications Surveys & Tutorials, 15(1), 293–311.Yassin, M., AboulHassan, M. A., Lahoud, S., Ibrahim, M., Mezher, D., Cousin, B., & Sourour, E. A. (2015). Survey of ICIC techniques in LTE networks under various mobile environment parameters. Wireless Networks, 1–16.Andrews, M., & Zhang, L. (2015). Utility optimization in heterogeneous networks via CSMA-based algorithms. Wireless Networks, 1–14.El-atty, S. M. A., & Gharsseldien, Z. M. (2016). Performance analysis of an advanced heterogeneous mobile network architecture with multiple small cell layers. Wireless Networks, 1–22.Huang, Q., Huang, Y.-C., Ko, K.-T., & Iversen, V. B. (2011). 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Multiservice loss models for broadband telecommunication networks. New York: Springer.Lin, Y.-B., & Mak, V. W. (1994). Eliminating the boundary effect of a large-scale personal communication service network simulation. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4(2), 165–190.Karray, M.K. (2010). Evaluation of the blocking probability and the throughput in the uplink of wireless cellular networks. In IEEE ComNet (pp. 1–8)

Scan statistics for the online detection of locally anomalous subgraphs

Identifying anomalies in computer networks is a challenging and complex problem. Often, anomalies occur in extremely local areas of the network. Locality is complex in this setting, since we have an underlying graph structure. To identify local anomalies, we introduce a scan statistic for data extracted from the edges of a graph over time. In the computer network setting, the data on these edges are multivariate measures of the communications between two distinct machines, over time. We describe two shapes for capturing locality in the graph: the star and the k-path. While the star shape is not new to the literature, the path shape, when used as a scan window, appears to be novel. Both of these shapes are motivated by hacker behaviors observed in real attacks. A hacker who is using a single central machine to examine other machines creates a star-shaped anomaly on the edges emanating from the central node. Paths represent traversal of a hacker through a network, using a set of machines in sequence. To identify local anomalies, these shapes are enumerated over the entire graph, over a set of sliding time windows. Local statistics in each window are compared with their historic behavior to capture anomalies within the window. These local statistics are model-based. To capture the communications between computers, we have applied two different models, observed and hidden Markov models, to each edge in the network. These models have been effective in handling various aspects of this type of data, but do not completely describe the data. Therefore, we also present ongoing work in the modeling of host-to-host communications in a computer network. Data speeds on larger networks require online detection to be nimble. We describe a full anomaly detection system, which has been applied to a corporate sized network and achieves better than real-time analysis speed. We present results on simulated data whose parameters were estimated from real network data. In addition, we present a result from our analysis of a real, corporate-sized network data set. These results are very encouraging, since the detection corresponded to exactly the type of behavior we hope to detect

Surrogate-based Analysis and Design Optimization of Power Delivery Networks

As microprocessor architectures continue to increase computing performance under low-energy consumption, the combination of signal integrity, electromagnetic interference, and power delivery is becoming crucial in the computer industry. In this context, power delivery engineers make use of complex and computationally expensive models that impose time-consuming industrial practices to reach an adequate power delivery design. In this paper, we propose a general surrogate-based methodology for fast and reliable analysis and design optimization of power delivery networks (PDN). We first formulate a generic surrogate model methodology exploiting passive lumped models optimized by parameter extraction to fit PDN impedance profiles. This PDN modeling formulation is illustrated with industrial laboratory measurements of a 4th generation server CPU motherboard. We next propose a black box PDN surrogate modeling methodology for efficient and reliable power delivery design optimization. To build our black box PDN surrogate, we compare four metamodeling techniques: support vector machines, polynomial surrogate modeling, generalized regression neural networks, and Kriging. The resultant best metamodel is then used to enable fast and accurate optimization of the PDN performance. Two examples validate our surrogate-based optimization approach: a voltage regulator with dual power rail remote sensing intended for communications and storage applications, by finding optimal sensing resistors and loading conditions; and a multiphase voltage regulator from a 6th generation Intel® server motherboard, by finding optimal compensation settings to reduce the number of bulk capacitors without losing CPU performance.ITESO, A.C

HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipments (UEs). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (non-uniformity) in the UE locations. Besides, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations. This paper presents a novel heterogeneous spatial traffic modeling which allows statistical adjustment. Simple and non-parameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second-moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross-moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).Comment: JSA

Network on Chip: a New Approach of QoS Metric Modeling Based on Calculus Theory

A NoC is composed by IP cores (Intellectual Propriety) and switches connected among themselves by communication channels. End-to-End Delay (EED) communication is accomplished by the exchange of data among IP cores. Often, the structure of particular messages is not adequate for the communication purposes. This leads to the concept of packet switching. In the context of NoCs, packets are composed by header, payload, and trailer. Packets are divided into small pieces called Flits. It appears of importance, to meet the required performance in NoC hardware resources. It should be specified in an earlier step of the system design. The main attention should be given to the choice of some network parameters such as the physical buffer size in the node. The EED and packet loss are some of the critical QoS metrics. Some real-time and multimedia applications bound up these parameters and require specific hardware resources and particular management approaches in the NoC switch. A traffic contract (SLA, Service Level Agreement) specifies the ability of a network or protocol to give guaranteed performance, throughput or latency bounds based on mutually agreed measures, usually by prioritizing traffic. A defined Quality of Service (QoS) may be required for some types of network real time traffic or multimedia applications. The main goal of this paper is, using the Network on Chip modeling architecture, to define a QoS metric. We focus on the network delay bound and packet losses. This approach is based on the Network Calculus theory, a mathematical model to represent the data flows behavior between IPs interconnected over NoC. We propose an approach of QoS-metric based on QoS-parameter prioritization factors for multi applications-service using calculus model