CN103826279A - Mobile data collection method with minimized concurrent data uploading and collecting cost - Google Patents

Abstract

The invention discloses a mobile data collection method with minimized concurrent data uploading and collecting cost. The mobile data collection method comprises the following steps: iteratively searching and computing an anchor point to optimally generate data size and a cost set thereof according to a data control subalgorithm (DCSA); carrying out parallel search and computing on flow rate, energy consumption cost and jamming cost according to a routing subalgorithm (RSA) so as to determine an optical data route from a sensor to a mobile collector (SenCar); searching an optimal data portioning proportion according to a data segmentation subalgorithm (DSS); carrying out parallel search on the transmission power of the sensor and the sojourn time of the mobile collector (SenCar) through power control and compatible algorithm (PCSA) and sojourn time allocation subalgorithm (STAS). According to the method, concurrent data uploading is permitted from the sensor to the mobile collector, thus the data collection delay is greatly shortened, the energy consumption is obviously reduced, and higher network throughput and effective resource utilization are obtained.

Description

There is concurrent data and upload and collect the mobile data collection method of Least-cost

Technical field

The invention belongs to network communications technology field, be specifically related to the movable data transmission method of wireless sensor network, especially relate to the mobile data collection method that one has concurrent data and uploads and collect Least-cost (DaGCM).

Background technology

The problem that mobile data collection exists for solving static data collection mode, in mobile data collection pattern, a kind of mobile node of specific type (is commonly referred to mobile collectors, in the present invention referred to as SenCar) be introduced into wireless sensor network, to make the connection between static sensor become easier, mobile collectors is born the routing function of remote transducer, because the some or all of route of transducer is born by mobile collectors, so the energy on transducer can be saved greatly, between transducer, the inhomogeneities of energy consumption also can be alleviated effectively, this wishes for the transducer with finite energy and storage resources very much.

In order to obtain Data Collection path more flexibly, researcher utilizes the method for dividing and ruling to propose a kind of trajectory path planning algorithm, this algorithm is to realize the recursively flex point in definite path of load balance, and for each portion of tissue of network is become to one bunch, researcher has considered mobility control, propose to produce ferry-boat path to meet stream demand and to minimize the algorithm of packet delay, proposed a kind of mobile node of dispatching to guarantee can not cause due to buffer overflow the algorithm of loss of data.Ekici etc. have proposed a kind of off-line heuritic approach, and the sensor-based data of this algorithm produce speed and their positional information is carried out computing cycle track to avoid loss of data in the time that low speed moves.

In above-mentioned existing mobile data collection mechanism, be all to adopt to collect data with the mobile collectors of individual antenna, and do not consider that the decline of channel is on the impact of message transmission rate, cause Data Collection to postpone large and energy consumption is higher.

Summary of the invention

Because the above-mentioned defect of prior art, technical problem to be solved by this invention is to provide the mobile data collection method that one has concurrent data and uploads and collect Least-cost (DaGCM), a transducer is defined as to this transducer and uploads in the residence time at this anchor point at mobile collectors the function of the data volume of mobile collectors in the Data Collection cost of an anchor point, the method allows concurrent uploading data from transducer to mobile collectors, thereby greatly shortening Data Collection postpones and reduces significantly energy consumption, obtain higher network throughput and the effectively utilization of resources.

For achieving the above object, the invention provides the mobile data collection method that one has concurrent data and uploads and collect Least-cost (DaGCM), concrete steps are as follows:

Step 1, produce data volume set according to Data Control subalgorithm (DCSA) iterative search calculating sensor at the optimum of each anchor point

and cost set

thereby determine the optimal data amount that each transducer produces in a Data Collection route;

Step 2, according to route subalgorithm (RSA) parallel search calculate flow rate

energy consumes cost with congested cost

thereby determine the optimal data route from transducer to mobile collectors; Find optimum Data Segmentation ratio by Data Segmentation subalgorithm (DSS)

Step 3, by power control and compatibility subalgorithm (PCSA) and the through-put power of distributing subalgorithm (STAS) parallel search calculating sensor residence time

with the residence time of mobile collectors

thereby the optimal transmission power of two transducer compatibility of definite assurance and mobile collectors are in the best residence time of each anchor point.

In preferred embodiments of the present invention, described Data Control subalgorithm (DCSA) concrete steps of described step 1 are:

Each transducer i obtains respectively the optimum generation data volume of transducer at each anchor point by the sub-gradient descent method of employing and according to formula (1) and (2) under the constraint of stream conservation, link capacity constraint

and cost

$y_i(k+1)={[y_i\left(k\right)+\mathrm{\ϵ}\left(k\right)\▿D_1\left(y_i\right)]}^{+}---\left(1\right)$

${\mathrm{\λ}}_i^a(k+1)={[{\mathrm{\λ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_1\left({\mathrm{\λ}}_i^a\right)]}^{+}---\left(2\right)$

Wherein

represent to produce data volume y about transducer i _isub-gradient,

nC ' _i(y _i) represent that cost function is about data volume y _ifirst derivative;

represent the cost about the transducer i of anchor point a

sub-gradient,

ε (k) represents iteration step length, is a constant.

In another preferred embodiments of the present invention, route subalgorithm described in described step 2 (RSA) concrete steps are:

Sensor node i receives from the data of described Data Control subalgorithm (DCSA) and produces cost

and input rate on every link, calculate the flow rate x of link (i, j) according to sub-gradient projection method _ij, energy consumes cost μ _{(i, m)}with congested cost v _ijas follows

$x_{\mathrm{ij}}(k+1)={[x_{\mathrm{ij}}\left(k\right)+\mathrm{\ϵ}\left(k\right)\▿D_2\left(x_{\mathrm{ij}}\right)]}^{+}---\left(3\right)$

${\mathrm{\μ}}_{(i,m)}(k+1)={[{\mathrm{\μ}}_{(i,m)}\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_2\left({\mathrm{\μ}}_{(i,m)}\right)]}^{+}---\left(4\right)$

$v_{\mathrm{ij}}(k+1)={[v_{\mathrm{ij}}\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_2\left(v_{\mathrm{ij}}\right)]}^{+}---\left(5\right)$

Wherein

with

represent respectively about flow rate x _ij, energy consumes cost μ _{(i, m)}with congested cost v _ijsub-gradient.When the stream of link (i, j) occurs when congested, the routed path that sensor node i just adjusts the data of the link (i, j) of flowing through reduces the flow x on this link _ijthereby, eliminate link congestion and fall low-energy-consumption, simultaneously by congested cost v _ijsend to power control subalgorithm and the mobile collectors (SenCar) of physical layer.

In preferred embodiments of the present invention,, Data Segmentation subalgorithm described in described step 2 (DSS) concrete steps are:

Data based on described Data Control subalgorithm (DCSA) produce cost

sensor node i is according to formula (6) and (7) calculated data amount of cutting apart

to determine the optimal data amount that uploads to anchor point a,

${\mathrm{\φ}}_i^a(k+1)={[{\mathrm{\φ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)({\mathrm{\λ}}_i^a/{\mathrm{\φ}}_i^a\left(k\right)+{\mathrm{\ω}}_i^a\left(k\right))]}^{+}---\left(6\right)$

${\mathrm{\ω}}_i^a(k+1)={[{\mathrm{\ω}}_i^a\left(k\right)+\mathrm{\ϵ}\left(k\right)({\mathrm{\φ}}_i^a\left(k\right)-1)]}^{+}---\left(7\right)$

In another preferred embodiments of the present invention, power control described in described step 3 and compatibility subalgorithm (PCSA) concrete steps are:

Measure from the link (i, j) of described route subalgorithm (RSA) and the congested cost v of (m, n) _ijand v _mn; The SINR value of measure link (i, j) and (m, n)

with

and the power grade p receiving _ih _ij, wherein p _irepresent the through-put power of sensor node i, h _ijrepresent the gain of link (i, j); Calculate message

and this message is passed to other sender; Sensor node i, m is according to message Msg _ij, Msg _mnand channel gain computes transmit power p _iand p _mif consistency constraint is satisfied, sensor node i and m be exactly one compatible right, just can transmit data simultaneously.

In another preferred embodiments of the present invention, distribute the described residence time in described step 3 subalgorithm (STAS) concrete steps to be:

Data based on Data Control subalgorithm described in described step 1 (DCSA) produce cost

sensor node i is according to formula (8) and (9) calculated data amount of cutting apart

to determine the optimal data amount that uploads to anchor point a,

${\mathrm{\φ}}_i^a(k+1)={[{\mathrm{\φ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)({\mathrm{\λ}}_i^a/{\mathrm{\φ}}_i^a\left(k\right)+{\mathrm{\ω}}_i^a\left(k\right))]}^{+}---\left(8\right)$

${\mathrm{\ω}}_i^a(k+1)={[{\mathrm{\ω}}_i^a\left(k\right)+\mathrm{\ϵ}\left(k\right)({\mathrm{\φ}}_i^a\left(k\right)-1)]}^{+}---\left(9\right)$

The mobile data collection method that there is concurrent data and upload and collect Least-cost that the present invention proposes, mobile collectors by use with many antennas and space division multiplexing (SDMA), propose first the associating cross-layer distributed algorithm being formed by Data Control subalgorithm, route subalgorithm, power control and compatibility subalgorithm, can greatly shorten Data Collection and postpone and reduce significantly energy consumption.

Below with reference to accompanying drawing, the technique effect of design of the present invention, concrete structure and generation is described further, to understand fully object of the present invention, feature and effect.

Accompanying drawing explanation

Fig. 1 is the mobile data collection method flow diagram of a preferred embodiment of the present invention.

Embodiment

The mobile data collection method that one has concurrent data uploaded and collected Least-cost (DaGCM), its flow process as shown in Figure 1, its basic principle is: transducer is defined as this transducer and uploads in the residence time at this anchor point at mobile collectors the function of the data volume of mobile collectors in the Data Collection cost of an anchor point, and anchor point refers to that mobile collectors stops and collects the place of ambient sensors data.Then with Lagrangian, the sub-Gradient Iteration algorithm of even summation is solved to DaGCM problem, and then an associating cross-layer distributed method being made up of Data Control subalgorithm, route subalgorithm, power control and compatible subalgorithm proposed, meanwhile, also provided definite mobile collectors in the different anchor points subalgorithm of optimum residence time.

As seen from Figure 1, the method is made up of three straton algorithms, respectively the Data Control subalgorithm DCSA of ground floor, the Data Segmentation subalgorithm DSS of the second layer and route subalgorithm RSA, the power control of the 3rd layer and compatibility subalgorithm PCSA and distribute subalgorithm STAS residence time;

At ground floor, DCSA iterative search calculated data duration set

with cost set thereby determine the optimal data amount that each transducer produces in a Data Collection route, and by produced cost

send to the second layer;

At the second layer, RSA parallel search calculates flow rate energy consumes cost

with congested cost

thereby determine the optimal data route from transducer to mobile data collection device (SenCar); DSS finds optimum Data Segmentation ratio

the congested cost simultaneously calculating

pass to the 3rd layer;

At the 3rd layer, the through-put power of PCSA and STAS parallel search calculating sensor

and the residence time of mobile collectors (SenCar)

thereby the optimal transmission power of two transducer compatibility of definite assurance and mobile collectors (SenCar) are in the best residence time of each anchor point.

Ground floor Data Control subalgorithm (DCSA) concrete steps are as follows:

Each transducer i obtains respectively in the optimum data volume that produces of anchor point by the sub-gradient descent method of employing and according to formula (1) and (2) under the constraint of stream conservation, link capacity constraint

and cost

$y_i(k+1)={[y_i\left(k\right)+\mathrm{\ϵ}\left(k\right)\▿D_1\left(y_i\right)]}^{+}---\left(1\right)$

${\mathrm{\λ}}_i^a(k+1)={[{\mathrm{\λ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_1\left({\mathrm{\λ}}_i^a\right)]}^{+}---\left(2\right)$

Wherein

represent to produce data volume y about transducer i _isub-gradient,

nC ' _i(y _i) represent that cost function is about data volume y _ifirst derivative;

represent the cost about the transducer i of anchor point a sub-gradient,

ε (k) represents iteration step length, is a constant.

Then, transducer i by upgrade after

pass to route subalgorithm RSA and the Data Segmentation subalgorithm DSS of the second layer.

Route subalgorithm (RSA) concrete steps of the second layer are as follows:

Sensor node i receives from the data of DCSA and produces cost

and input rate on every link, calculate the flow rate x of link (i, j) according to sub-gradient projection method _ij, energy consumes cost μ _{(i, m)}with congested cost v _ijas follows

$x_{\mathrm{ij}}(k+1)={[x_{\mathrm{ij}}\left(k\right)+\mathrm{\ϵ}\left(k\right)\▿D_2\left(x_{\mathrm{ij}}\right)]}^{+}---\left(3\right)$

${\mathrm{\μ}}_{(i,m)}(k+1)={[{\mathrm{\μ}}_{(i,m)}\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_2\left({\mathrm{\μ}}_{(i,m)}\right)]}^{+}---\left(4\right)$

$v_{\mathrm{ij}}(k+1)={[v_{\mathrm{ij}}\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_2\left(v_{\mathrm{ij}}\right)]}^{+}---\left(5\right)$

Wherein

with

represent respectively about flow rate xi _j, energy consumes cost μ _{(i, m)}with congested cost v _ijsub-gradient.When the stream of link (i, j) occurs when congested, the routed path that sensor node i just adjusts the data of the link (i, j) of flowing through reduces the flow x on this link _ijthereby, eliminate link congestion and fall low-energy-consumption.Simultaneously by congested cost v _ijsend to power control subalgorithm and the mobile collectors (SenCar) of physical layer.

Data Segmentation subalgorithm (DSS) concrete steps are as follows:

Data based on DCSA produce cost

sensor node i is according to formula (6) and (7) calculated data amount of cutting apart

to determine the optimal data amount that uploads to anchor point a,

${\mathrm{\φ}}_i^a(k+1)={[{\mathrm{\φ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)({\mathrm{\λ}}_i^a/{\mathrm{\φ}}_i^a\left(k\right)+{\mathrm{\ω}}_i^a\left(k\right))]}^{+}---\left(6\right)$

${\mathrm{\ω}}_i^a(k+1)={[{\mathrm{\ω}}_i^a\left(k\right)+\mathrm{\ϵ}\left(k\right)({\mathrm{\φ}}_i^a\left(k\right)-1)]}^{+}---\left(7\right)$

The power control of the 3rd layer and compatibility subalgorithm (PCSA) concrete steps are as follows:

Measure from the link (i, j) of RSA and the congested cost v of (m, n) _ijand v _mn;

The SINR value of measure link (i, j) and (m, n)

with

and the power grade p receiving _ih _ij, wherein p _irepresent the through-put power of sensor node i, h _ijrepresent the gain of link (i, j);

Calculate message and this message is passed to other sender;

Sensor node i, m is according to message Msg _ij, Msg _mnand channel gain computes transmit power p _iand p _mif consistency constraint is satisfied, sensor node i and m be exactly one compatible right, just can transmit data simultaneously.

Distribute residence time subalgorithm (STAS) concrete steps as follows:

Based on the congested cost v of RSA _ijwith maximum residence time of T, mobile collectors (SenCar) determines that according to formula (8) (9) it is at t best residence time of anchor point a ^a:

More than describe preferred embodiment of the present invention in detail.The ordinary skill that should be appreciated that this area just can design according to the present invention be made many modifications and variations without creative work.Therefore, all technical staff in the art, all should be in by the determined protection range of claims under this invention's idea on the basis of existing technology by the available technical scheme of logical analysis, reasoning, or a limited experiment.

Claims (6)

1. there is concurrent data and upload and collect a mobile data collection method for Least-cost, it is characterized in that, comprise the steps:

Step 1, produce data volume set according to Data Control subalgorithm (DCSA) iterative search calculating sensor at the optimum of each anchor point and cost set

thereby determine the optimal data amount that each transducer produces in a Data Collection route;

Step 2, according to route subalgorithm (RSA) parallel search calculate flow rate

energy consumes cost

with congested cost

thereby determine the optimal data route from transducer to mobile collectors; Find optimum Data Segmentation ratio by Data Segmentation subalgorithm (DSS)

Step 3, by power control and compatibility subalgorithm (PCSA) and the through-put power of distributing subalgorithm (STAS) parallel search calculating sensor residence time with the residence time of mobile collectors

thereby the optimal transmission power of two transducer compatibility of definite assurance and mobile collectors are in the best residence time of each anchor point.

2. the mobile data collection method that has concurrent data and upload and collect Least-cost according to claim 1, is characterized in that, described Data Control subalgorithm (DCSA) concrete steps in described step 1 are:

Each transducer i obtains respectively the optimum generation data volume of transducer at each anchor point by the sub-gradient descent method of employing and according to formula (1) and (2) under the constraint of stream conservation, link capacity constraint

and cost

$y_i(k+1)={[y_i\left(k\right)+\mathrm{\ϵ}\left(k\right)\▿D_1\left(y_i\right)]}^{+}---\left(1\right)$

${\mathrm{\λ}}_i^a(k+1)={[{\mathrm{\λ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_1\left({\mathrm{\λ}}_i^a\right)]}^{+}---\left(2\right)$

Wherein

represent to produce data volume y about transducer i _isub-gradient, nC ' _i(y _i) represent that cost function is about data volume y _ifirst derivative;

represent the cost about the transducer i of anchor point a

sub-gradient,

ε (k) represents iteration step length, is a constant.

3. the mobile data collection method that has concurrent data and upload and collect Least-cost according to claim 2, is characterized in that, route subalgorithm described in described step 2 (RSA) concrete steps are:

Sensor node i receives from the data of described Data Control subalgorithm (DCSA) and produces cost

and input rate on every link, calculate the flow rate x of link (i, j) according to sub-gradient projection method _ij, energy consumes cost μ _{(i, m)}with congested cost v _ijas follows

$x_{\mathrm{ij}}(k+1)={[x_{\mathrm{ij}}\left(k\right)+\mathrm{\ϵ}\left(k\right)\▿D_2\left(x_{\mathrm{ij}}\right)]}^{+}---\left(3\right)$

${\mathrm{\μ}}_{(i,m)}(k+1)={[{\mathrm{\μ}}_{(i,m)}\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_2\left({\mathrm{\μ}}_{(i,m)}\right)]}^{+}---\left(4\right)$

$v_{\mathrm{ij}}(k+1)={[v_{\mathrm{ij}}\left(k\right)-\mathrm{\ϵ}\left(k\right)\▿D_2\left(v_{\mathrm{ij}}\right)]}^{+}---\left(5\right)$

Wherein

with

represent respectively about flow rate x _ij, energy consumes cost μ _{(i, m)}with congested cost v _ijsub-gradient; When the stream of link (i, j) occurs when congested, the routed path that sensor node i just adjusts the data of the link (i, j) of flowing through reduces the flow x on this link _ijthereby, eliminate link congestion and fall low-energy-consumption, simultaneously by congested cost v _ijsend to power control subalgorithm and the mobile collectors (SenCar) of physical layer.

4. the mobile data collection method that has concurrent data and upload and collect Least-cost according to claim 2, is characterized in that, Data Segmentation subalgorithm described in described step 2 (DSS) concrete steps are:

Data based on described Data Control subalgorithm (DCSA) produce cost

sensor node i is according to formula (6) and (7) calculated data amount of cutting apart

to determine the optimal data amount that uploads to anchor point a,

${\mathrm{\φ}}_i^a(k+1)={[{\mathrm{\φ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)({\mathrm{\λ}}_i^a/{\mathrm{\φ}}_i^a\left(k\right)+{\mathrm{\ω}}_i^a\left(k\right))]}^{+}---\left(6\right)$

${\mathrm{\ω}}_i^a(k+1)={[{\mathrm{\ω}}_i^a\left(k\right)+\mathrm{\ϵ}\left(k\right)({\mathrm{\φ}}_i^a\left(k\right)-1)]}^{+}---\left(7\right).$

5. the mobile data collection method that has concurrent data and upload and collect Least-cost according to claim 3, is characterized in that, power control described in described step 3 and compatibility subalgorithm (PCSA) concrete steps are:

Measure from the link (i, j) of described route subalgorithm (RSA) and the congested cost v of (m, n) _ijand v _mn; The SINR value of measure link (i, j) and (m, n)

with

and the power grade p receiving _ih _ij, wherein p _irepresent the through-put power of sensor node i, h _ijrepresent the gain of link (i, j); Calculate message

and this message is passed to other sender; Sensor node i, m is according to message Msg _ij, Msg _mnand channel gain computes transmit power p _iand p _mif consistency constraint is satisfied, sensor node i and m be exactly one compatible right, just can transmit data simultaneously.

6. the mobile data collection method that has concurrent data and upload and collect Least-cost according to claim 3, is characterized in that, distributes residence time described in described step 3 subalgorithm (STAS) concrete steps to be:

Data based on Data Control subalgorithm described in described step 1 (DCSA) produce cost

sensor node i is according to formula (8) and (9) calculated data amount of cutting apart

to determine the optimal data amount that uploads to anchor point a,

${\mathrm{\φ}}_i^a(k+1)={[{\mathrm{\φ}}_i^a\left(k\right)-\mathrm{\ϵ}\left(k\right)({\mathrm{\λ}}_i^a/{\mathrm{\φ}}_i^a\left(k\right)+{\mathrm{\ω}}_i^a\left(k\right))]}^{+}---\left(8\right)$

${\mathrm{\ω}}_i^a(k+1)={[{\mathrm{\ω}}_i^a\left(k\right)+\mathrm{\ϵ}\left(k\right)({\mathrm{\φ}}_i^a\left(k\right)-1)]}^{+}---\left(9\right).$