QoS Analysis for a Non-Preemptive Continuous Monitoring and Event Driven WSN Protocol in Mobile Environments

Evolution in wireless sensor networks (WSNs) has allowed the introduction of new applications with increased complexity regarding communication protocols, which have to ensure that certain QoS parameters are met. Specifically, mobile applications require the system to respond in a certain manner in order to adequately track the target object. Hybrid algorithms that perform Continuous Monitoring (CntM) and Event-Driven (ED) duties have proven their ability to enhance performance in different environments, where emergency alarms are required. In this paper, several types of environments are studied using mathematical models and simulations, for evaluating the performance of WALTER, a priority-based nonpreemptive hybrid WSN protocol that aims to reduce delay and packet loss probability in time-critical packets. First, randomly distributed events are considered. This environment can be used to model a wide variety of physical phenomena, for which report delay and energy consumption are analyzed by means of Markov models. Then, mobile-only environments are studied for object tracking purposes. Here, some of the parameters that determine the performance of the system are identified. Finally, an environment containing mobile objects and randomly distributed events is considered. It is shown that by assigning high priority to time-critical packets, report delay is reduced and network performance is enhanced.This work was partially supported by CONACyT under Project 183370. The research of Vicent Pla has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R.Leyva Mayorga, I.; Rivero-Angeles, ME.; Carreto-Arellano, C.; Pla, V. (2015). QoS Analysis for a Non-Preemptive Continuous Monitoring and Event Driven WSN Protocol in Mobile Environments. International Journal of Distributed Sensor Networks. 2015:1-16. https://doi.org/10.1155/2015/471307S1162015Arampatzis, T., Lygeros, J., & Manesis, S. (s. f.). A Survey of Applications of Wireless Sensors and Wireless Sensor Networks. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005. doi:10.1109/.2005.1467103Ramachandran, C., Misra, S., & Obaidat, M. S. (2008). A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks. International Journal of Communication Systems, 21(10), 1047-1073. doi:10.1002/dac.937Misra, S., Singh, S., Khatua, M., & Obaidat, M. S. (2013). Extracting mobility pattern from target trajectory in wireless sensor networks. International Journal of Communication Systems, 28(2), 213-230. doi:10.1002/dac.2649Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. doi:10.1109/twc.2002.804190Younis, O., & Fahmy, S. (s. f.). Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach. IEEE INFOCOM 2004. doi:10.1109/infcom.2004.1354534Manjeshwar, A., & Agrawal, D. P. (s. f.). TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001. doi:10.1109/ipdps.2001.925197Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless. Proceedings 16th International Parallel and Distributed Processing Symposium. doi:10.1109/ipdps.2002.1016600Sharif, A., Potdar, V., & Rathnayaka, A. J. D. (2010). Prioritizing Information for Achieving QoS Control in WSN. 2010 24th IEEE International Conference on Advanced Information Networking and Applications. doi:10.1109/aina.2010.166Alappat, V. J., Khanna, N., & Krishna, A. K. (2011). Advanced Sensor MAC protocol to support applications having different priority levels in Wireless Sensor Networks. 2011 6th International ICST Conference on Communications and Networking in China (CHINACOM). doi:10.1109/chinacom.2011.6158175Alam, K. M., Kamruzzaman, J., Karmakar, G., & Murshed, M. (2012). Priority Sensitive Event Detection in Hybrid Wireless Sensor Networks. 2012 21st International Conference on Computer Communications and Networks (ICCCN). doi:10.1109/icccn.2012.6289220Raja, A., & Su, X. (2008). A Mobility Adaptive Hybrid Protocol for Wireless Sensor Networks. 2008 5th IEEE Consumer Communications and Networking Conference. doi:10.1109/ccnc08.2007.159Srikanth, B., Harish, M., & Bhattacharjee, R. (2011). An energy efficient hybrid MAC protocol for WSN containing mobile nodes. 2011 8th International Conference on Information, Communications & Signal Processing. doi:10.1109/icics.2011.6173629Lee, Y.-D., Jeong, D.-U., & Lee, H.-J. (2011). Empirical analysis of the reliability of low-rate wireless u-healthcare monitoring applications. International Journal of Communication Systems, 26(4), 505-514. doi:10.1002/dac.1360Deepak, K. S., & Babu, A. V. (2013). Improving energy efficiency of incremental relay based cooperative communications in wireless body area networks. International Journal of Communication Systems, 28(1), 91-111. doi:10.1002/dac.2641Yuan Li, Wei Ye, & Heidemann, J. (s. f.). Energy and latency control in low duty cycle MAC protocols. IEEE Wireless Communications and Networking Conference, 2005. doi:10.1109/wcnc.2005.1424589Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210Wei Ye, Heidemann, J., & Estrin, D. (s. f.). An energy-efficient MAC protocol for wireless sensor networks. Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. doi:10.1109/infcom.2002.101940

Performance evaluation of wireless sensor networks for mobile event and mobile sink

Extending lifetime and energy efficiency are important objectives and challenges in-Wireless Sensor Networks (WSNs). In large scale WSNs, when the nodes are near to the sink they consume much more energy than the nodes far from the sink. In our previous work, we considered that the sink node was stationary and only event node was moving in the observation field. In this work, we consider both cases when the sink node and event node are moving. For the simulations, we use TwoRayGround and Shadowing radio models, lattice topology and AODV protocol. We compare the simulation results for the cases when the sink node and event node are mobile and stationary. The simulation results have shown that the goodput of TwoRayGround is better than Shadowing in case of mobile event, but the depletion of Shadowing is better than TwoRayGround in case of mobile event. The goodput in case of mobile sink is better than stationary sink when the transmission rate is lower than 10pps. For TwoRayGround radio model, the depletion in case of mobile sink is better than stationary sink when the number of nodes is increasedPeer ReviewedPostprint (published version

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

AWARE: Platform for Autonomous self-deploying and operation of Wireless sensor-actuator networks cooperating with unmanned AeRial vehiclEs

This paper presents the AWARE platform that seeks to enable the cooperation of autonomous aerial vehicles with ground wireless sensor-actuator networks comprising both static and mobile nodes carried by vehicles or people. Particularly, the paper presents the middleware, the wireless sensor network, the node deployment by means of an autonomous helicopter, and the surveillance and tracking functionalities of the platform. Furthermore, the paper presents the first general experiments of the AWARE project that took place in March 2007 with the assistance of the Seville fire brigades

Improving energy efficiency in wireless sensor networks through scheduling and routing

This paper is about the wireless sensor network in environmental monitoring applications. A Wireless Sensor Network consists of many sensor nodes and a base station. The number and type of sensor nodes and the design protocols for any wireless sensor network is application specific. The sensor data in this application may be light intensity, temperature, pressure, humidity and their variations .Clustering and routing are the two areas which are given more attention in this paper.Comment: 7 Pages, 2 Figures and 1 Tabl