CN117202325B - Self-adaptive sensing and cooperative transmission method in industrial Internet of things - Google Patents
Self-adaptive sensing and cooperative transmission method in industrial Internet of things Download PDFInfo
- Publication number
- CN117202325B CN117202325B CN202311051272.XA CN202311051272A CN117202325B CN 117202325 B CN117202325 B CN 117202325B CN 202311051272 A CN202311051272 A CN 202311051272A CN 117202325 B CN117202325 B CN 117202325B
- Authority
- CN
- China
- Prior art keywords
- sensor
- transmission
- data
- edge estimator
- scheduling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 212
- 238000000034 method Methods 0.000 title claims abstract description 61
- 230000008447 perception Effects 0.000 claims abstract description 35
- 230000006870 function Effects 0.000 claims description 33
- 238000005265 energy consumption Methods 0.000 claims description 17
- 230000009365 direct transmission Effects 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 10
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 238000012546 transfer Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000009825 accumulation Methods 0.000 claims description 3
- 239000000654 additive Substances 0.000 claims description 3
- 230000000996 additive effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000012886 linear function Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 abstract description 7
- 238000004088 simulation Methods 0.000 description 6
- 238000012733 comparative method Methods 0.000 description 5
- 230000001953 sensory effect Effects 0.000 description 3
- 238000010835 comparative analysis Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a self-adaptive sensing and cooperative transmission method in an industrial Internet of things, which belongs to the technical field of wireless communication and comprises the following steps: the cooperative transmission of sensor scheduling and sensing information is jointly designed, an automatic guided trolley assisted cooperative transmission architecture is provided, and an analytical expression of estimation errors is provided in an iterative mode; introducing an estimation error difference value to evaluate the influence of sensor scheduling on the estimation error, and designing a low-complexity sensor scheduling strategy to minimize the estimation error; the transmission mode and communication resources of the perception information are jointly optimized, and the self-adaptive cooperative transmission strategy can effectively improve the transmission performance of the perception information. The invention can determine the sensor scheduling strategy according to the contribution degree of the perception information to the estimation performance, and can adaptively adjust the transmission mode of the perception information, thereby effectively improving the precision of state estimation.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a self-adaptive sensing and cooperative transmission method in the industrial Internet of things.
Background
In industrial internet of things systems, sensors are widely deployed to collect sensory data and then use the sensory data communicated over a wireless channel for state estimation to infer an operational state of an industrial process. In general, more perceptual data can provide more accurate state estimates. However, due to limited network communication resources, when the network is congested, perceived data may be lost, thereby reducing the accuracy of the estimation and wasting communication resources. Furthermore, in severe industrial environments, severe fading and complex interference may lead to increased transmission delay and even transmission failure, which may lead to larger estimation errors. Thus, resource limitations and environmental factors need to be considered in communicating the perceptual data to ensure accurate state estimation.
It follows that the state estimation performance in an industrial environment is strongly dependent on the transmission performance of perceived data back via the industrial wireless network. In the field of industrial wireless networks, the smart state estimation algorithm is designed to relieve the influence of wireless transmission on estimation performance, for example, aiming at the random packet loss condition in a network system, the estimation error can be effectively reduced by designing the matrix fusion estimation algorithm. However, the design of the state estimation algorithm can only passively alleviate the influence of the unfavorable transmission environment on the estimation performance, and once the packet loss rate exceeds a certain level, the transmission reliability still cannot alleviate the abrupt decline of the estimation performance. Therefore, finding an efficient transmission method to actively improve the transmission reliability of the network is critical to ensure the performance of the state estimation. Cooperative transmission has become an important technique for improving transmission reliability by utilizing an additional degree of spatial freedom to combat a severe transmission environment. The prior researches show that under the same transmission condition, the cooperative transmission can obviously improve the transmission reliability and the energy efficiency. However, in an industrial internet of things system, additional nodes placed for relay purposes only may not always be ideal or cost effective. And as the use of the automatic guiding trolley in the industrial field increases, it is necessary to explore the influence of the automatic guiding trolley as a relay node on the transmission performance of the industrial internet of things system.
In summary, the problems of the prior art are: because of the complexity of the industrial wireless transmission environment, it is not always efficient to simply design a state estimation algorithm to improve estimation performance; the node which is only set for relay in the cooperative transmission technology is not always ideal for the industrial internet of things system, and whether the automatic guiding trolley with the relay capability can promote the perceived data transmission of the industrial internet of things system is yet to be verified.
Disclosure of Invention
According to the technical problems, the self-adaptive sensing and cooperative transmission method in the industrial Internet of things is provided.
The invention adopts the following technical means: an adaptive sensing and cooperative transmission method in an industrial Internet of things comprises the following steps:
S1, providing an automatic guided trolley assisted cooperative transmission architecture, and providing an analytical expression of an estimation error of an industrial Internet of things system in an iterative manner;
S2, analyzing influence of sensor scheduling on estimation errors, and providing a sensor scheduling algorithm based on the difference value of the estimation errors to minimize the estimation errors;
s3, a self-adaptive cooperative transmission strategy is designed, so that the transmission energy consumption of the sensor is reduced while the transmission performance of the sensing information is improved.
Further, the step S1 specifically includes:
S11, in order to avoid transmission congestion of the sensor, only the sensor scheduled by the edge estimator transmits sensing data, and a binary variable xi {0,1} is adopted to represent a sensor scheduling index;
Wherein, if ζ n (k) =1, it means that the nth sensor is scheduled by the edge estimator at the kth time step;
S12, in order to avoid interference among data transmission perceived by different sensors, the sensors select different sub-channels to transmit data, and binary variables theta epsilon {0,1} are adopted to represent sub-channel allocation indexes;
Wherein, if θ n,m (k) =1, it means that the nth sensor performs the sensing data transmission on the mth sub-channel at the kth time step;
S13, the scheduled sensor is divided into two transmission modes according to whether the transmission sensing data are transmitted by the sensor through the relay transmission of the automatic guiding trolley:
Mode one: the sensor directly transmits the perception data to the edge estimator in a direct transmission mode;
Mode two: a coordinated transmission scheme in which a time step is divided into two phases; in the first stage, the sensor transmits the perception data to the edge estimator and the automatic guiding trolley at the same time; in the second stage, the automatic guiding trolley transmits the received perception data to the edge estimator in an amplifying and forwarding mode; finally, the edge estimator combines the data received in the two stages by using a maximum ratio combining technology;
S14, two transmission modes of the sensor are represented by a binary variable delta epsilon {0,1}, and if delta n (k) =1, the n-th sensor selects a cooperative transmission mode. According to two transmission modes, the total data transmission delay from the nth sensor to the edge estimator is expressed as:
S15, the unscheduled sensor will be in a sleep state with negligible energy loss. The transmission energy consumption of the sensor perceived data is expressed as:
Thus, the remaining energy of the nth sensor at the kth time step is:
wherein, Is the remaining energy of the nth sensor at the end of the (k-1) th time step;
S16, in view of data transmission through a lossy wireless channel, a phenomenon of data loss occurs in the transmission of perception information. Thus, let the binary variable ζ ε {0,1} represent the data transfer state, specifically:
Where τ is the duration of one time step, if ζ n (k) =1 indicates that the edge estimator successfully receives the sensing data transmitted by the nth sensor;
S17, according to the scheduling condition of the sensor and the data transmission condition of the perception information, the estimated value of the system at the kth time step edge estimator is as follows:
Where A is the state transition matrix, F is the Kalman filter gain, Is the available observation of the edge estimator,Is the aggregate observations of the edge estimator,Is an aggregate observation matrix that is configured to be a plurality of observation matrices,Is the observation matrix, Λ (k) =diag { ζ 1(k)ζ1(k)I1(k),...,ξN(k)ζN(k)IN (k) };
s18, estimating errors of the edge estimator at the kth time step are as follows:
Wherein w (k-1) is Gaussian white noise with a mean value of 0 and a variance of Q w, Is the aggregate white gaussian noise with a mean of 0 and a variance of Q v. Trace using mean square estimation errorTo evaluate the estimation accuracy of the edge estimator.
Further, the first mode: the sensor directly transmits the perception data to the edge estimator in a direct transmission mode;
1) Based on the channel conditions between the sensor and the edge estimator, the n-th sensor to edge estimator signal-to-noise ratio is expressed as:
Wherein the method comprises the steps of Is the transmit power of the nth sensor on the mth subchannel,Representing the signal-to-noise ratio per unit power of the sensor to edge estimator transmission link,Representing the channel gain of the nth sensor on the mth subchannel, σ 2 being the power of the additive white gaussian noise;
2) The data transfer rate achievable by the nth sensor to edge estimator is expressed as:
Wherein B is the transmission bandwidth of the subchannel;
3) The data transmission delay from the nth sensor to the edge estimator is expressed as:
Where q n (k) is the data size of the nth sensor sense data.
Further, the second mode: a coordinated transmission scheme in which a time step is divided into two phases; in the first stage, the sensor transmits the perception data to the edge estimator and the automatic guiding trolley at the same time; in the second stage, the automatic guiding trolley transmits the received perception data to the edge estimator in an amplifying and forwarding mode; the process of combining the data received by the edge estimator at two stages by using the maximum ratio combining technique is as follows:
1) Based on the channel conditions between the sensor and the edge estimator, the n-th sensor to edge estimator signal-to-noise ratio is expressed as:
wherein, The signal to noise ratio of the relay transmission link of the trolley is automatically guided, and the expression is as follows:
wherein, The automated guided vehicle forwards the transmit power of the nth sensor perceived data on the mth subcarrier,Is the signal to noise ratio per unit power from the nth sensor to the automated guided vehicle,Is the unit power channel ratio that automatically directs the cart to the edge estimator;
2) The data transfer rate achieved by the nth sensor-to-edge estimator is expressed as:
3) The data transmission delay from the nth sensor to the edge estimator is shown as.
Further, the influence of the sensor scheduling on the estimation error is analyzed, and a sensor scheduling algorithm based on the difference value of the estimation error is provided to minimize the estimation error, which is specifically as follows:
s21, jointly optimizing sensor scheduling and sensing data transmission, and minimizing a mean square estimation error of state estimation:
Wherein C1 represents a number constraint of scheduled sensors; c2 represents that at most one subchannel can be allocated to only one sensor; c3 represents a single-subchannel transmission; c4 and C5 are the transmit power constraints of the sensor and the automated guided vehicle, respectively; C6-C8 are the viable ranges of the indicated variables; c9 means that sensors that do not meet the minimum remaining energy requirement cannot be scheduled;
solving the established problem model to obtain a sensor scheduling strategy and a participatory perception data transmission scheme which minimize the mean square estimation error;
S22, as the sensor scheduling decision and the perception data transmission are tightly coupled on each time step, and the objective function contains the time accumulation sum of K time steps, constraint conditions are specific to each time step, and therefore P 0 is decomposed into a plurality of instant sub-problems;
S23, on the kth time step, a time sequence relation exists between sensor scheduling and sensing data transmission; therefore, the problem P 0 is decomposed into two sub-problems to be solved, namely a sensor scheduling sub-problem and a perception data transmission sub-problem; the sensor scheduling sub-problem is expressed as:
S24, in order to effectively solve the problem SP 1, an estimation error difference e Δ (k) is proposed to measure the contribution of sensor scheduling to reducing the mean square estimation error, as follows:
wherein, Representing the estimated error when all sensors are not scheduled,Representing an estimated error when all sensors are scheduled and the perceptual data is successfully received by the edge estimator;
S25, in view of the estimated error difference, the problem SP 1 is rewritten as:
wherein, The problem SP 1.1 is a 0-1 integer programming problem, and in order to solve the problem SP 1.1 efficiently, a sensor scheduling algorithm based on the estimated error difference is proposed for solving.
S26, the perceived data transmission sub-problem is expressed as:
wherein, Representing a set of sensors that are scheduled; an adaptive cooperative transmission strategy is designed to solve the problem SP 2.
Further, the sensor scheduling algorithm based on the estimated error difference is as follows:
1) Calculating e 0 (k) and e 1 (k) and updating the estimated error difference e Δ (k);
2) Counting the number N cs of the residual energy of the sensor meeting the minimum residual energy requirement;
3) When N cs≥Nmin, sequentially scheduling the sensors with the largest estimated error difference e Δ (k) until the number of the scheduled sensors is equal to min { M, N cs };
4) When N cs<Nmin, scheduling is stopped.
Further, the self-adaptive cooperative transmission strategy is designed specifically as follows:
S31, analyzing an objective function aiming at a problem SP 2;
First, aggregate The base of (2) determines the upper bound of the objective function, and the amount of perceived data successfully received by the edge estimator determines the gap between the objective function and its upper bound;
Second, the energy consumption of the sensor for data transmission will affect the collection In order to guarantee the upper bound of the objective function, the energy consumption of the data transmission should be reduced as much as possible, and furthermore, the transmission state of the perceived data depends on its transmission delay according to step S16, and thus, the amount of perceived data successfully received by the edge estimator is measured by the transmission delay constraint (t n (k). Based on the above analysis, the problem SP 2 is rewritten as:
Wherein C10 is a rate expression of the transmission delay constraint;
S32, since the objective function of the problem SP 2.1 is a monotonic function with respect to the transmission power, the problem SP 2.1 is rewritten as:
S33, solving the problem SP2.2 by iteratively optimizing a transmission mode, sub-channel allocation and transmitting power, wherein the objective function of the problem is a linear function;
S331, under the premise of given sub-channel allocation and transmitting power, the sensor selects a transmission mode for realizing a larger transmission rate, and the transmission mode is specifically as follows:
S332, on the premise of given transmission mode and transmission power, the achieved transmission rate is only affected by the allocation of the sub-channels, and a variable ω n,m (k) is introduced to evaluate the achievable transmission rates under different sub-channel allocations, as follows:
Wherein, penalty factors are introduced to avoid transmission rates less than the minimum value, and then the subchannel allocation problem is expressed as:
Problem SP 2.3 is a 0-1 linear programming problem, and the optimal solution of the problem is obtained through a KM algorithm;
As is known from the constraint C10 in step S31, the problem of solving the transmit power is different in different transmission modes, and therefore, the power solutions in the direct transmission mode and the cooperative transmission mode are given below:
1) The power solving problem under the direct transmission mode is as follows:
This is a convex problem, which can be solved using standard convex optimization methods,
2) The power solving problem under the cooperative transmission mode is as follows:
wherein, The method is that a sensor set of a cooperative transmission mode is selected by the scheduled sensors, and the Lagrange method is adopted to effectively solve the problem.
Further, the specific solving process of the power solving problem in the cooperative transmission mode is as follows:
i) The Lagrangian function of the problem SP 2.5 is given:
wherein λ S,λA and λ R are non-negative lagrange multipliers.
Ii) according to the KKT condition, the optimal solution of the lagrangian function in i) is at zero function gradient, i.e.:
iii) Simultaneous solving of the equations in ii) yields the optimal transmit power for the sensor and the automated guided vehicle, respectively, as follows:
iii) by iteratively updating the lagrangian multiplier to obtain the optimal solution for problem SP 2.5.
The invention comprises the following steps: the cooperative transmission of sensor scheduling and sensing information is jointly designed, an automatic guided trolley assisted cooperative transmission architecture is provided, and an analytical expression of estimation errors is provided in an iterative mode; introducing an estimation error difference value to evaluate the influence of sensor scheduling on the estimation error, and designing a low-complexity sensor scheduling strategy to minimize the estimation error; the transmission mode and communication resources of the perception information are jointly optimized, and the self-adaptive cooperative transmission strategy can effectively improve the transmission performance of the perception information. Compared with the prior art, the invention has the following advantages:
The self-adaptive sensing and cooperative transmission method in the industrial Internet of things can adaptively adjust the transmission mode of sensing data according to the wireless channel condition and the energy resource in a complex and changeable industrial network system, and effectively improves the reliability of data transmission of the industrial network system.
For the reasons, the invention can be widely popularized in the fields of wireless communication and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a network scenario diagram used in an embodiment of the present invention.
Fig. 3 is a flowchart of a sensor scheduling algorithm based on an estimated error difference according to an embodiment of the present invention.
Fig. 4 is a graph of mean square estimation error versus the different methods provided by the embodiments of the present invention.
Fig. 5 is a graph of average energy consumption versus various methods provided by embodiments of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, the embodiment of the invention provides a self-adaptive sensing and cooperative transmission method in an industrial internet of things, which comprises the following steps:
s1, providing an automatic guided trolley assisted cooperative transmission architecture, and providing an analytical expression of an estimation error in an iterative manner;
S2, analyzing influence of sensor scheduling on estimation errors, and providing a sensor scheduling algorithm based on the difference value of the estimation errors to minimize the estimation errors;
s3, a self-adaptive cooperative transmission strategy is designed, so that the transmission energy consumption of the sensor is reduced while the transmission performance of the sensing information is improved.
Fig. 2 is a network scenario diagram used in an embodiment of the present invention.
In the industrial internet of things system, N sensors are deployed to monitor the operational status of the device and transmit collected sensory data to an edge estimator. Then, the edge estimator performs state estimation to infer the operation state of the device based on the received perception data. In the considered scenario, the automated guided vehicle acts as a relay, and the received perceived data may be forwarded to an edge estimator, thereby improving the transmission performance of the data.
Further, the proposed collaborative transmission architecture assisted by the automatic guided vehicle provides an analytical expression for estimating an error in an iterative manner, which specifically includes:
S11, in order to avoid transmission congestion of the sensor, only the sensor scheduled by the edge estimator transmits sensing data, and a binary variable xi {0,1} is adopted to represent a sensor scheduling index.
Where ζ n (k) =1, it means that the nth sensor is scheduled by the edge estimator at the kth time step.
S12, in order to avoid interference among data transmission perceived by different sensors, the sensors select different sub-channels to transmit data, and a binary variable theta epsilon {0,1} is adopted to represent a sub-channel allocation index.
Where θ n,m (k) =1, it indicates that the nth sensor performs the sensing data transmission on the mth subchannel at the kth time step.
S13, the scheduled sensor is divided into two transmission modes according to whether the transmission sensing data are transmitted by the sensor through the relay transmission of the automatic guiding trolley:
Mode one: in a direct transmission mode, the sensor directly transmits the perception data to the edge estimator.
1) Based on the channel conditions between the sensor and the edge estimator, the n-th sensor to edge estimator signal-to-noise ratio is expressed as:
Wherein the method comprises the steps of Is the transmit power of the nth sensor on the mth subchannel,Representing the signal-to-noise ratio per unit power of the sensor to edge estimator transmission link,Representing the channel gain of the nth sensor on the mth subchannel, σ 2 is the power of the additive white gaussian noise.
2) The data transfer rate achievable by the nth sensor to edge estimator is expressed as:
where B is the transmission bandwidth of the subchannel.
3) The data transmission delay from the nth sensor to the edge estimator is expressed as:
Where q n (k) is the data size of the nth sensor sense data.
Mode two: and a coordinated transmission scheme in which one time step is divided into two phases. In the first stage, the sensor transmits the perception data to the edge estimator and the automatic guiding trolley at the same time; in the second stage, the automatic guiding trolley transmits the received perception data to the edge estimator in an amplifying and forwarding mode; finally, the edge estimator combines the data received in the two stages using a maximum ratio combining technique.
1) Based on the channel conditions between the sensor and the edge estimator, the n-th sensor to edge estimator signal-to-noise ratio is expressed as:
wherein, The signal to noise ratio of the relay transmission link of the trolley is automatically guided, and the expression is as follows:
wherein, The automated guided vehicle forwards the transmit power of the nth sensor perceived data on the mth subcarrier,Is the signal to noise ratio per unit power from the nth sensor to the automated guided vehicle,Is the unit power channel ratio that automatically directs the cart to the edge estimator.
2) The data transfer rate achievable by the nth sensor to edge estimator is expressed as:
3) The data transmission delay from the nth sensor to the edge estimator is expressed as:
S14, two transmission modes of the sensor are represented by a binary variable delta epsilon {0,1}, and if delta n (k) =1, the n-th sensor selects a cooperative transmission mode. According to two transmission modes, the total data transmission delay from the nth sensor to the edge estimator is expressed as:
S15, the unscheduled sensor will be in a sleep state with negligible energy loss. The transmission energy consumption of the sensor perceived data is expressed as:
Thus, the remaining energy of the nth sensor at the kth time step is:
wherein, Is the remaining energy of the nth sensor at the end of the (k-1) th time step.
S16, in view of data transmission through a lossy wireless channel, a phenomenon of data loss occurs in the transmission of perception information. Thus, let the binary variable ζ ε {0,1} represent the data transfer state, specifically:
Where τ is the duration of one time step, if ζ n (k) =1 indicates that the edge estimator successfully receives the sensing data transmitted by the nth sensor.
S17, according to the scheduling condition of the sensor and the data transmission condition of the perception information, the estimated value of the system at the kth time step edge estimator is as follows:
Where A is the state transition matrix, F is the Kalman filter gain, Is the available observation of the edge estimator,Is the aggregate observations of the edge estimator,Is an aggregate observation matrix that is configured to be a plurality of observation matrices,Is the observation matrix, Λ (k) =diag { ζ 1(k)ζ1(k)I1(k),...,ξN(k)ζN(k)IN (k) }.
S18, estimating errors of the edge estimator at the kth time step are as follows:
Wherein w (k-1) is Gaussian white noise with a mean value of 0 and a variance of Q w, Is the aggregate white gaussian noise with a mean of 0 and a variance of Q v. Trace using mean square estimation errorTo evaluate the estimation accuracy of the edge estimator.
Fig. 3 is a flowchart of a sensor scheduling algorithm based on an estimated error difference according to an embodiment of the present invention.
Further, the influence of the sensor scheduling on the estimation error is analyzed, and a sensor scheduling algorithm based on the difference value of the estimation error is provided to minimize the estimation error, which is specifically as follows:
s21, jointly optimizing sensor scheduling and sensing data transmission, and minimizing a mean square estimation error of state estimation:
Wherein C1 represents a number constraint of scheduled sensors; c2 represents that at most one subchannel can be allocated to only one sensor; c3 represents a single-subchannel transmission; c4 and C5 are the transmit power constraints of the sensor and the automated guided vehicle, respectively; C6-C8 are the viable ranges of the indicated variables; c9 indicates that sensors that do not meet the minimum remaining energy requirement cannot be scheduled.
And solving the established problem model to obtain a sensor scheduling strategy and a participatory perception data transmission scheme which minimize the mean square estimation error.
S22, since the sensor scheduling decision and the perceived data transmission are tightly coupled at each time step, and the objective function contains a time accumulation sum of K time steps, the constraint is for each time step, P 0 is decomposed into a plurality of transient sub-problems herein.
S23, at the kth time step, a time sequence relation exists between the sensor scheduling and the sensing data transmission. Thus, the problem P 0 is decomposed into two sub-problems to solve, a sensor scheduling sub-problem and a perceived data transmission sub-problem, respectively. The sensor scheduling sub-problem is expressed as:
this is a non-convex problem that cannot be solved directly.
S24, in order to effectively solve the problem SP 1, an estimation error difference e Δ (k) is proposed to measure the contribution of sensor scheduling to reducing the mean square estimation error, as follows:
wherein, Representing the estimated error when all sensors are not scheduled,Representing the estimated error when all sensors are scheduled and the perceptual data is successfully received by the edge estimator.
S25, in view of the estimated error difference, the problem SP 1 can be rewritten as:
wherein, Problem SP 1.1 is a 0-1 integer programming problem, and the optimal solution can be obtained by using an exhaustion algorithm. However, the computational complexity of the exhaustive algorithm is exponential. In order to solve the problem SP 1.1 efficiently, we propose a sensor scheduling algorithm based on the estimated error difference, see step S26 for details.
S26, a sensor scheduling algorithm based on the estimated error difference is as follows:
1) Calculating e 0 (k) and e 1 (k) and updating the estimated error difference e Δ (k);
2) Counting the number N cs of the residual energy of the sensor meeting the minimum residual energy requirement;
3) When N cs≥Nmin, sequentially scheduling the sensors with the largest estimated error difference e Δ (k) until the number of the scheduled sensors is equal to min { M, N cs };
4) When N cs<Nmin, scheduling is stopped.
S27, the perceived data transmission sub-problem is expressed as:
wherein, Representing the scheduled set of sensors. Problem SP 2 is a mixed integer nonlinear programming problem that is difficult to solve directly. In order to solve the problem SP 2 effectively, we have designed an adaptive cooperative transmission strategy, see step S3 for details.
Further, the adaptive cooperative transmission strategy specifically includes:
s31, analyzing an objective function aiming at the problem SP 2. First, aggregate The base of (2) determines the upper bound of the objective function and the amount of perceptual data successfully received by the edge estimator determines the gap of the objective function from the upper bound. Second, the energy consumption of the sensor for data transmission will affect the collectionIs a base of (c). Therefore, in order to ensure the upper bound of the objective function, the energy consumption of data transmission should be reduced as much as possible. Furthermore, the transmission state of the perceived data depends on its transmission delay, as described in step S16. Thus, the amount of perceived data successfully received by the edge estimator can be measured by the propagation delay constraint (t n (k. Ltoreq.τ). Based on the above analysis, the problem SP 2 can be rewritten as:
Wherein C10 is a rate-expressed form of the propagation delay constraint.
S32, since the objective function of the problem SP 2.1 is a monotonic function with respect to the transmission power, the problem SP 2.1 can be rewritten as:
The objective function of the problem SP2.2 is a linear function, but the non-convex constraint makes it difficult to solve the problem directly. To solve the problem efficiently, the problem is solved by iteratively optimizing the transmission scheme, sub-channel allocation and transmit power.
S331, under the premise of given sub-channel allocation and transmitting power, the sensor selects a transmission mode capable of realizing a larger transmission rate, and the transmission mode is specifically as follows:
S332, on the premise of given transmission mode and transmission power, the achievable transmission rate is only affected by the sub-channel allocation. For ease of representation, the variable ω n,m (k) is introduced to evaluate the achievable transmission rates for different sub-channel allocations, as follows:
wherein a penalty factor is introduced to avoid transmission rates less than a minimum. The subchannel allocation problem may then be expressed as:
Problem SP 2.3 is a 0-1 linear programming problem, and the best solution of the problem is obtained through KM algorithm.
S333, it is known from the constraint C10 in step S31 that the problem of solving the transmit power is different in different transmission modes. Therefore, the following gives the power solutions in direct transmission mode and cooperative transmission mode, respectively:
1) The power solving problem under the direct transmission mode is as follows:
this is a convex problem that can be solved using standard convex optimization methods.
2) The power solving problem under the cooperative transmission mode is as follows:
wherein, Is a set of sensors selected by the scheduled sensor to co-transmit. Problem SP 2.5 is difficult to solve directly because constraint C12 is a non-convex constraint. We use the lagrangian method to solve this problem effectively, with the following specific solution process:
i) The Lagrangian function of the problem SP 2.5 is given:
wherein λ S,λA and λ R are non-negative lagrange multipliers.
Ii) according to the KKT condition, the optimal solution of the lagrangian function in i) is at zero function gradient, i.e.:
iii) Simultaneous solving of the equations in ii) yields the optimal transmit power for the sensor and the automated guided vehicle, respectively, as follows:
iii) by iteratively updating the lagrangian multiplier to obtain the optimal solution for problem SP 2.5.
In order to verify the effectiveness of the method of the present invention, the effect of the application of the present invention will be described in detail with reference to simulation.
Simulation conditions
In the simulation scenario, the monitoring range in the industrial environment is an 80m by 300m rectangular area, and 30 sensors are randomly arranged in the range. The bandwidth of the sub-channel is 180KHz, the number of the sub-channels is 12, the maximum transmission power of the sensor is 10dBm, the maximum transmission power of the automatic guiding trolley is 20dBm, and the minimum number of the sensors which can be scheduled is 8. The Gaussian white noise power spectral density is-42 dBm/Hz in the industrial environment, the sensing data size of each sensor is 1.5-2.5Kbits, and the length of one estimation interval is 100ms.
Simulation content and result analysis
The effectiveness of the method of the present invention was verified by comparison with the following four transmission methods.
Comparison method 1: in the direct transmission method, the scheduled sensors all select a direct transmission mode to transmit the sensing data.
Comparison method 2: a random scheduling method, which randomly produces sensors scheduled in each estimation step.
Comparison method 3: a random sub-channel allocation method that randomly allocates sub-channels to scheduled sensors.
Comparison method 4: maximum transmit power method in which the scheduled sensors do not perform power optimization and all transmit data at maximum transmit power.
Simulation 1: the comparative analysis is based on mean square estimation errors of different methods.
As can be seen from fig. 4, the mean square estimation error of the comparative method 4 is very close to that of the proposed method. The mean square estimation error of the comparison method 1 is slightly larger than that of the proposed method, so that the self-adaptive cooperative transmission strategy can be shown to be capable of effectively reducing the estimation error compared with the direct transmission strategy. Whereas the mean square estimation error of comparative method 2 and comparative method 3 is significantly larger than the proposed method, wherein the mean square estimation error of comparative method 2 is the largest, which indicates that sensor scheduling is more important in improving estimation performance than subchannel allocation.
Simulation 2: the comparative analysis is based on the average energy consumption of the different methods.
As can be seen in fig. 5, the average energy consumption of comparative method 1 is greater than that of the proposed method. Therefore, compared with a direct transmission mode, the self-adaptive cooperative transmission strategy can effectively reduce the transmission energy consumption of the sensor so as to prolong the service life of the whole system. The comparison method 4 also has an average energy consumption greater than the proposed method, since the sensor's transmit power is not optimized.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (5)
1. The self-adaptive sensing and cooperative transmission method in the industrial Internet of things is characterized by comprising the following steps of:
S1, providing an automatic guided trolley assisted cooperative transmission architecture, and providing an analytical expression of an estimation error of an industrial Internet of things system in an iterative manner;
the step S1 specifically comprises the following steps:
S11, in order to avoid transmission congestion of the sensor, only the sensor scheduled by the edge estimator transmits sensing data, and a binary variable xi {0,1} is adopted to represent a sensor scheduling index;
Wherein, if ζ n (k) =1, it means that the nth sensor is scheduled by the edge estimator at the kth time step;
S12, in order to avoid interference among data transmission perceived by different sensors, the sensors select different sub-channels to transmit data, and binary variables theta epsilon {0,1} are adopted to represent sub-channel allocation indexes;
Wherein, if θ n,m (k) =1, it means that the nth sensor performs the sensing data transmission on the mth sub-channel at the kth time step;
S13, the scheduled sensor is divided into two transmission modes according to whether the transmission sensing data are transmitted by the sensor through the relay transmission of the automatic guiding trolley:
Mode one: the sensor directly transmits the perception data to the edge estimator in a direct transmission mode;
Mode two: a coordinated transmission scheme in which a time step is divided into two phases; in the first stage, the sensor transmits the perception data to the edge estimator and the automatic guiding trolley at the same time; in the second stage, the automatic guiding trolley transmits the received perception data to the edge estimator in an amplifying and forwarding mode; finally, the edge estimator combines the data received in the two stages by using a maximum ratio combining technology;
s14, adopting a binary variable delta epsilon {0,1} to represent two transmission modes of the sensor, and if delta n (k) =1, representing that the nth sensor selects a cooperative transmission mode; according to two transmission modes, the total data transmission delay from the nth sensor to the edge estimator is expressed as:
S15, enabling the sensor which is not scheduled to be in a dormant state, wherein the energy loss is negligible; the transmission energy consumption of the sensor perceived data is expressed as:
Thus, the remaining energy of the nth sensor at the kth time step is:
wherein, Is the remaining energy of the nth sensor at the end of the (k-1) th time step;
S16, in view of data transmission through a lossy wireless channel, a phenomenon of data loss occurs in the transmission of perception information; thus, let the binary variable ζ ε {0,1} represent the data transfer state, specifically:
Where τ is the duration of one time step, if ζ n (k) =1 indicates that the edge estimator successfully receives the sensing data transmitted by the nth sensor;
S17, according to the scheduling condition of the sensor and the data transmission condition of the perception information, the estimated value of the system at the kth time step edge estimator is as follows:
Where A is the state transition matrix, F is the Kalman filter gain, Is the available observation of the edge estimator,Is the aggregate observations of the edge estimator,Is an aggregate observation matrix that is configured to be a plurality of observation matrices,Is the observation matrix, Λ (k) =diag { ζ 1(k)ζ1(k)I1(k),...,ξN(k)ζN(k)IN (k) };
s18, estimating errors of the edge estimator at the kth time step are as follows:
Wherein w (k-1) is Gaussian white noise with a mean value of 0 and a variance of Q w, Is the trace of mean value 0 and variance Q v aggregate Gaussian white noise and adopts mean square estimation errorTo evaluate an estimation accuracy of the edge estimator;
S2, analyzing influence of sensor scheduling on estimation errors, and providing a sensor scheduling algorithm based on the difference value of the estimation errors to minimize the estimation errors;
The influence of the sensor scheduling on the estimation error is analyzed, and a sensor scheduling algorithm based on the estimation error difference is provided to minimize the estimation error, which is specifically as follows:
s21, jointly optimizing sensor scheduling and sensing data transmission, and minimizing a mean square estimation error of state estimation:
P0:
s.t.C 1:
C2:
C3:
C4:
C5
C6:
C7:
C8:
C9:
Wherein C1 represents a number constraint of scheduled sensors; c2 represents that at most one subchannel can be allocated to only one sensor; c3 represents a single-subchannel transmission; c4 and C5 are the transmit power constraints of the sensor and the automated guided vehicle, respectively; C6-C8 are the viable ranges of the indicated variables; c9 means that sensors that do not meet the minimum remaining energy requirement cannot be scheduled;
solving the established problem model to obtain a sensor scheduling strategy and a participatory perception data transmission scheme which minimize the mean square estimation error;
S22, as the sensor scheduling decision and the perception data transmission are tightly coupled on each time step, and the objective function contains the time accumulation sum of K time steps, constraint conditions are specific to each time step, and therefore P 0 is decomposed into a plurality of instant sub-problems;
S23, on the kth time step, a time sequence relation exists between sensor scheduling and sensing data transmission; therefore, the problem P 0 is decomposed into two sub-problems to be solved, namely a sensor scheduling sub-problem and a perception data transmission sub-problem; the sensor scheduling sub-problem is expressed as:
SP1:
s.t.C1,C8,C9
S24, in order to effectively solve the problem SP 1, an estimation error difference e Δ (k) is proposed to measure the contribution of sensor scheduling to reducing the mean square estimation error, as follows:
wherein, Representing the estimated error when all sensors are not scheduled,Representing an estimated error when all sensors are scheduled and the perceptual data is successfully received by the edge estimator;
S25, in view of the estimated error difference, the problem SP 1 is rewritten as:
SP1.1:
s.t.C1,C8,C9
wherein, The problem SP 1.1 is a 0-1 integer programming problem, in order to solve the problem SP 1.1 efficiently, a sensor scheduling algorithm based on the estimated error difference is proposed to solve,
S26, the perceived data transmission sub-problem is expressed as:
SP2:
s.t.C2':
C3':
C4':
C5'
C6':
C7':
wherein, Representing a set of sensors that are scheduled; an adaptive cooperative transmission strategy is designed, and the problem SP2 is solved;
s3, a self-adaptive cooperative transmission strategy is designed, so that the transmission energy consumption of the sensor is reduced while the transmission performance of the sensing information is improved;
The self-adaptive cooperative transmission strategy is specifically designed as follows:
S31, analyzing an objective function aiming at a problem SP 2;
First, aggregate The base of (2) determines the upper bound of the objective function, and the amount of perceived data successfully received by the edge estimator determines the gap between the objective function and its upper bound;
Second, the energy consumption of the sensor for data transmission will affect the collection In order to guarantee the upper bound of the objective function, the energy consumption of the data transmission should be reduced as much as possible, and in addition, according to the step S16, the transmission state of the perceived data depends on the transmission delay, so the quantity of the perceived data successfully received by the edge estimator is measured by the transmission delay constraint t n (k). Ltoreq.τ; based on the above analysis, the problem SP 2 is rewritten as:
s.t.C2'-C7',
C10:
Wherein C10 is a rate expression of the transmission delay constraint;
S32, since the objective function of the problem SP 2.1 is a monotonic function with respect to the transmission power, the problem SP 2.1 is rewritten as:
SP2.2:
s.t.C2'-C7',C10
S33, solving the problem SP2.2 by iteratively optimizing a transmission mode, sub-channel allocation and transmitting power, wherein the objective function of the problem is a linear function;
S331, under the premise of given sub-channel allocation and transmitting power, the sensor selects a transmission mode for realizing a larger transmission rate, and the transmission mode is specifically as follows:
S332, on the premise of given transmission mode and transmission power, the achieved transmission rate is only affected by the allocation of the sub-channels, and a variable ω n,m (k) is introduced to evaluate the achievable transmission rates under different sub-channel allocations, as follows:
where ω 0 is a penalty factor introduced to avoid transmission rates below a minimum, then the subchannel allocation problem is expressed as:
SP2.3:
s.t.C2',C3',C6'
Problem SP 2.3 is a 0-1 linear programming problem, and the optimal solution of the problem is obtained through a KM algorithm;
As is known from the constraint C10 in step S31, the problem of solving the transmit power is different in different transmission modes, and therefore, the power solutions in the direct transmission mode and the cooperative transmission mode are given below:
1) The power solving problem under the direct transmission mode is as follows:
SP2.4:
s.t.C4',
C11:
This is a convex problem, solved using standard convex optimization methods,
2) The power solving problem under the cooperative transmission mode is as follows:
SP2.5:
s.t.C4”:
C5”:
C12:
wherein, The method is that a sensor set of a cooperative transmission mode is selected by the scheduled sensors, and the Lagrange method is adopted to effectively solve the problem.
2. The method for adaptive sensing and cooperative transmission in the industrial internet of things according to claim 1, wherein the first mode is: the sensor directly transmits the perception data to the edge estimator in a direct transmission mode;
1) Based on the channel conditions between the sensor and the edge estimator, the n-th sensor to edge estimator signal-to-noise ratio is expressed as:
Wherein the method comprises the steps of Is the transmit power of the nth sensor on the mth subchannel,Representing the signal-to-noise ratio per unit power of the sensor to edge estimator transmission link,Representing the channel gain of the nth sensor on the mth subchannel, σ 2 being the power of the additive white gaussian noise;
2) The data transfer rate achievable by the nth sensor to edge estimator is expressed as:
Wherein B is the transmission bandwidth of the subchannel;
3) The data transmission delay from the nth sensor to the edge estimator is expressed as:
Where q n (k) is the data size of the nth sensor sense data.
3. The method for adaptive sensing and cooperative transmission in the industrial internet of things according to claim 1, wherein the second mode is: a coordinated transmission scheme in which a time step is divided into two phases; in the first stage, the sensor transmits the perception data to the edge estimator and the automatic guiding trolley at the same time; in the second stage, the automatic guiding trolley transmits the received perception data to the edge estimator in an amplifying and forwarding mode; the process of combining the data received by the edge estimator at two stages by using the maximum ratio combining technique is as follows:
1) Based on the channel conditions between the sensor and the edge estimator, the n-th sensor to edge estimator signal-to-noise ratio is expressed as:
wherein, The signal to noise ratio of the relay transmission link of the trolley is automatically guided, and the expression is as follows:
wherein, The automated guided vehicle forwards the transmit power of the nth sensor perceived data on the mth subcarrier,Is the signal to noise ratio per unit power from the nth sensor to the automated guided vehicle,Is the unit power channel ratio that automatically directs the cart to the edge estimator;
2) The data transfer rate achieved by the nth sensor-to-edge estimator is expressed as:
3) The data transmission delay from the nth sensor to the edge estimator is shown as.
4. The method for adaptive sensing and cooperative transmission in the industrial internet of things according to claim 1, wherein the sensor scheduling algorithm based on the estimated error difference is as follows:
1) Calculating e 0 (k) and e 1 (k) and updating the estimated error difference e Δ (k);
2) Counting the number N cs of the residual energy of the sensor meeting the minimum residual energy requirement;
3) When N cs≥Nmin, sequentially scheduling the sensors with the largest estimated error difference e Δ (k) until the number of the scheduled sensors is equal to min { M, N cs };
4) When N cs<Nmin, scheduling is stopped.
5. The method for adaptive sensing and cooperative transmission in the industrial internet of things according to claim 1, wherein the specific solving process of the power solving problem in the cooperative transmission mode is as follows:
i) The Lagrangian function of the problem SP 2.5 is given:
wherein λ S,λA and λ R are non-negative lagrange multipliers;
ii) according to the KKT condition, the optimal solution of the lagrangian function in i) is at zero function gradient, i.e.:
iii) Simultaneous solving of the equations in ii) yields the optimal transmit power for the sensor and the automated guided vehicle, respectively, as follows:
iii) by iteratively updating the lagrangian multiplier to obtain the optimal solution for problem SP 2.5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311051272.XA CN117202325B (en) | 2023-08-21 | 2023-08-21 | Self-adaptive sensing and cooperative transmission method in industrial Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311051272.XA CN117202325B (en) | 2023-08-21 | 2023-08-21 | Self-adaptive sensing and cooperative transmission method in industrial Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117202325A CN117202325A (en) | 2023-12-08 |
| CN117202325B true CN117202325B (en) | 2024-07-09 |
Family
ID=88986086
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311051272.XA Active CN117202325B (en) | 2023-08-21 | 2023-08-21 | Self-adaptive sensing and cooperative transmission method in industrial Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117202325B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115714793A (en) * | 2022-11-08 | 2023-02-24 | 大连海事大学 | Method for transmitting perception information in industrial Internet of things on demand |
| CN116436984A (en) * | 2023-04-06 | 2023-07-14 | 华北电力大学 | Edge-end cooperative distributed energy regulation and control method and system for time synchronization error perception |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115801829B (en) * | 2022-11-08 | 2023-09-05 | 大连海事大学 | Edge self-adaptive sensing method in industrial Internet of things |
-
2023
- 2023-08-21 CN CN202311051272.XA patent/CN117202325B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115714793A (en) * | 2022-11-08 | 2023-02-24 | 大连海事大学 | Method for transmitting perception information in industrial Internet of things on demand |
| CN116436984A (en) * | 2023-04-06 | 2023-07-14 | 华北电力大学 | Edge-end cooperative distributed energy regulation and control method and system for time synchronization error perception |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117202325A (en) | 2023-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ortiz et al. | Reinforcement learning for energy harvesting decode-and-forward two-hop communications | |
| Xiao et al. | Power scheduling of universal decentralized estimation in sensor networks | |
| CN101682363A (en) | System and methods for controlling modem hardware | |
| Senol et al. | Performance of distributed estimation over unknown parallel fading channels | |
| US20140106801A1 (en) | Transceiver operating in a wireless communications network, a system and method for transmission in the network | |
| Ozel et al. | Resource management for fading wireless channels with energy harvesting nodes | |
| CN104702557A (en) | Incomplete CSI (Channel State Information)-based distributed antenna system adaptive modulation method | |
| Levorato et al. | A learning framework for cognitive interference networks with partial and noisy observations | |
| Khalil et al. | Bit error rate performance analysis in amplify-and-forward relay networks | |
| Ouyang et al. | Scheduling with rate adaptation under incomplete knowledge of channel/estimator statistics | |
| Liu et al. | Dynamic power optimization of pilot and data for downlink OFDMA systems | |
| CN117202325B (en) | Self-adaptive sensing and cooperative transmission method in industrial Internet of things | |
| Majid Butt et al. | Relay selection schemes to minimise outage in wireless powered communication networks | |
| Varma et al. | Energy efficient time-triggered control over wireless sensor/actuator networks | |
| Toorchi et al. | Fast and low-complexity reinforcement learning for delay-sensitive energy harvesting wireless visual sensing systems | |
| Ksairi et al. | Near-optimal resource allocation for type-II HARQ based OFDMA networks under rate and power constraints | |
| Qasmi et al. | Fixed rate statistical qos provisioning for Markovian sources in machine type communication | |
| Jiao et al. | Queue performance of energy harvesting cognitive radio sensor networks with cooperative spectrum sharing | |
| Hong et al. | Channel-aware random access control for distributed estimation in sensor networks | |
| Yuan et al. | Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks | |
| Wang et al. | Feedback power control with bit error outage probability QoS measure on the Rayleigh fading channel | |
| Ortiz et al. | Throughput maximization in two-hop energy harvesting communications | |
| Gandhi et al. | A cross layer design for performance enhancements in LTE-A system | |
| Robert et al. | Effective cooperative spectrum sensing using deep recurrent reinforced learning‐based Q‐routing in multihop cognitive radio networks | |
| Shang et al. | Real-time transmission mechanism design for wireless iot sensors with energy harvesting under power saving mode |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |