Cross-layered and Interactive Techniques for Energy Minimization of M2M Networks

  • Lin C.
  • Venkateswaran V.

Machine to Machine (M2M) networks are severely constrained by the overall energy available for sensing and communication at sensor nodes. The existing techniques minimize energy focus on specific network components and optimize its energy consumption. This paper explores a unified approach to optimize for energy consumption in the overall M2M network. We consider challenges in the physical communication design, medium access control techniques and network topology of M2M networks that are designed to serve heterogeneous applications. We propose a set of cross-layered techniques specifying the optimal amount of time that each sensor node must transmit, sleep and access the network to minimize overall energy. Subsequently, we propose a dynamic technique to optimize the abovementioned resources for changes in traffic scenarios. We observe the performance of cross-layered and dynamic optimization techniques via numerical and network simulations and show exceptional amount of energy savings.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...