Saudi Arabian Oil Company

Truva Corporation

Future Wei Technologies, Inc.

Shih-Chun Lin

Ian F Akyildiz

Howard Khan Schmidt

Abdallah Awadh Al-Shehri

Min Luo

Abbdullah Awadh Al-Shehri

Example computer-implemented methods, computer-readable media, and computer systems are described for providing communication protocol architecture or framework for magnetic induction (MI)-based communications in wireless underground sensor networks (WUSNs), for example, in underground oil reservoirs. In some aspects, environment information of an underground region that affects the transmission qualities of MI communications is evaluated. A protocol stack is identified. The protocol stack includes a number of layers for MI communications among a number of sensors in a WUSN in the underground region. A cross-layer framework and the distributed protocol are built to jointly optimize communication functionalities of the plurality of layers based on the evaluation.

Environment-aware cross-layer communication protocol in underground oil reservoirs

Example computer-implemented methods, computer-readable media, and computer systems are described for accurate localization of wireless sensor devices in underground oil reservoirs. In some aspects, every sensor measures respective received magnetic field strengths (RMFSs) on a plurality of respective magnetic induction (MI) links and transmits the measured respective RMFSs to at least one anchor devices. A set of distances is determined from the measured respective RMFSs. The set of distances is processed through an ordered sequence of algorithms, namely weighted maximum likelihood estimation (WMLE), semi-definite programming (SDP) relaxation, alternating direction augmented Lagrangian method (ADM), and conjugate gradient algorithm (CGA), to generate accurate localization of the wireless sensor devices in underground oil reservoirs.

Magnetic induction based localization for wireless sensor networks in underground oil reservoirs

A network element (NE) comprising a receiver configured to receive packets from a plurality of flows, and a processor coupled to the receiver and configured to perform classification on the plurality of flows according to arrivals of the packets to classify the plurality of flows into a heavy-tailed (HT) class and a light-tailed (LT) class assign scheduling weights to the plurality of flows according to the classification, and select a scheduling policy to forward the packets of the plurality of flows according to the scheduling weights.

Maximize network capacity policy with heavy-tailed traffic

A method includes obtaining traffic statistics of accepted hybrid traffic at a controller of a software defined network that includes multiple local switches coupled by links, calculating an end to end delay associated with the accepted hybrid traffic, determining network stability thresholds from each local switch as a function of network congestion, determining an adjusted rate decision policy as a function of the end to end delay and the network stability thresholds, the adjusted rate decision policy for use by an edge switch to determine whether or not to accept offered traffic loads.

Software defined network traffic congestion control

A method includes determining a number and placement of multiple controllers in a software defined network (SDN) such that each controller controls a different set of software controlled switches in the SDN and finding optimal forwarding paths for control traffic between the switches and controllers to minimize delay of control traffic over the software defined network.

Multi-controller control traffic balancing in software defined networks

Example computer-implemented methods, computer-readable media, and computer systems are described for accurate localization of wireless sensor devices in underground oil reservoirs. In some aspects, every sensor measures respective received magnetic field strengths (RMFSs) on a plurality of respective magnetic induction (MI) links and transmits the measured respective RMFSs to at least one anchor devices. A set of distances is determined from the measured respective RMFSs. The set of distances is processed through an ordered sequence of algorithms, namely weighted maximum likelihood estimation (WMLE), semi-definitec programming (SDP) relaxation, alternating direction augmented Lagrangian method (ADM), and conjugate gradient algorithm (CGA), to generate accurate localization of the wireless sensor devices in underground oil reservoirs.

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Alpharetta, GA, United States of America

Shih-Chun Lin