Environment-aware cross-layer communication protocol in underground oil reservoirs
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.
1. A method comprising:
identifying, by each of a plurality of sensors in a wireless underground sensor network (WUSN) in an underground region, a plurality of environment-dependent parameters measured by the plurality of sensors;
identifying, by each of the plurality of sensors, respective communication functions for a plurality of layers of a protocol stack for magnetic induction (MI) communications among the plurality of sensors in the WUSN in the underground region;
identifying, by each of the plurality of sensors, an optimization problem for jointly optimizing the respective communication functions of the plurality of layers of the protocol stack based on the plurality of environment-dependent parameters, the optimization problem including a plurality of transmission parameters defining the respective communication functions of the plurality of layers of the protocol stack;
determining, by each of the plurality of sensors, the plurality of transmission parameters by solving the optimization problem; and
transmitting, by each of the plurality of sensors based on magnetic induction, signals using the plurality of transmission parameters defining the respective communication functions of the plurality of layers of the protocol stack.
2. The method of claim 1 , wherein the protocol stack is a three-layer protocol stack that includes a physical layer, a data link layer, and a network layer.
3. The method of claim 2 , wherein identifying a communication function for each layer of a protocol stack comprises:
identifying a direct sequence code division multiple access (DS-CDMA) scheme as a multiple access scheme for the data link layer; and
identifying a geographic routing algorithm as a routing scheme for the network layer.
4. The method of claim 2 , wherein solving the optimization problem comprises one or more of:
performing a distributed power control based on a non-cooperative game theory;
evaluating a relation between a chaotic code of the DS-CDMA scheme and a link throughput for the data link layer of the protocol stack; or
identifying a forwarder for a transmitter of a transceiver coil pair according to the geographic routing algorithm by performing a two-phase decision process.
5. The method of claim 1 , wherein the plurality of environment-dependent parameters comprises a temperature, an electrical conductivity, a magnetic permeability, and a coil resistance.
6. The method of claim 1 , further comprising evaluating effects of the plurality of environment-dependent parameters on quality of the MI communications among the plurality of sensors in the underground region.
7. The method of claim 1 , wherein the transmission parameters comprises one or more of a modulation scheme, a coding scheme, a transmitted power level, a data rate, a coding length of chaotic code, or a next-hop packet forwarder.
8. The method of claim 1 , wherein the optimization problem further comprises one or more communication constraints based on a quality of service requirement, a power control requirement, or both.
9. The method of claim 1 , wherein the optimization problem comprises a multi-objective optimization problem, an application-driven optimization problem, or a combination of a multi-objective optimization problem and an application-driven optimization problem by a weight sum.
10. The method of claim 1 , wherein the optimization problem comprises one or more optimization objectives that comprise a minimum energy consumption, a maximum network throughput, or both.
11. The method of claim 1 , further comprising performing a random access scheme when the transmitting of the signals fails.