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2018-06-11
Zayene, M., Habachi, O., Meghdadi, V., Ezzeddine, T., Cances, J. P..  2017.  Joint delay and energy minimization for Wireless Sensor Networks using instantly decodable network coding. 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). :21–25.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

2017-04-20
Lee, Joohyun, Lee, Kyunghan, Jeong, Euijin, Jo, Jaemin, Shroff, Ness B..  2016.  Context-aware Application Scheduling in Mobile Systems: What Will Users Do and Not Do Next? Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. :1235–1246.

Usage patterns of mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness can be the key to make mobile systems to become personalized and situation dependent in managing their resources. We first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find context-dependency in application usage patterns, for which we classify contexts in a personalized manner with unsupervised learning methods. Using the knowledge acquired, we develop a novel context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications in a timely manner. Our trace-driven simulations with 96 user traces demonstrate the benefits of CAS over existing algorithms. We also verify the practicality of CAS by implementing it on the Android platform.