CN116506877B - Distributed collaborative computing method for mobile crowd sensing - Google Patents
Distributed collaborative computing method for mobile crowd sensing Download PDFInfo
- Publication number
- CN116506877B CN116506877B CN202310753432.9A CN202310753432A CN116506877B CN 116506877 B CN116506877 B CN 116506877B CN 202310753432 A CN202310753432 A CN 202310753432A CN 116506877 B CN116506877 B CN 116506877B
- Authority
- CN
- China
- Prior art keywords
- equipment
- assisting
- task
- steps
- cluster
- 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
- 238000004364 calculation method Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000013468 resource allocation Methods 0.000 claims abstract description 34
- 238000005457 optimization Methods 0.000 claims abstract description 25
- 238000005265 energy consumption Methods 0.000 claims abstract description 24
- 230000011218 segmentation Effects 0.000 claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 18
- 230000009977 dual effect Effects 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 6
- 241000854291 Dianthus carthusianorum Species 0.000 claims description 3
- 238000013459 approach Methods 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 230000001174 ascending effect Effects 0.000 claims description 2
- 239000000243 solution Substances 0.000 description 21
- 230000006870 function Effects 0.000 description 4
- 230000008447 perception Effects 0.000 description 3
- 230000004083 survival effect Effects 0.000 description 3
- 241000764238 Isis Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000009440 infrastructure construction Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
- H04W28/09—Management thereof
- H04W28/0925—Management thereof using policies
-
- 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
Abstract
The invention discloses a distributed collaborative computing method for mobile crowd sensing, which belongs to the technical field of wireless communication and comprises the following steps: s1: the whole network equipment exchanges position and electric quantity information, and the assisting equipment is matched with the task equipment according to the information to complete the allocation of the assisting equipment so as to form an assisting cluster; s2: in each assistant cluster, decomposing the weighted energy consumption and the minimization problem into two sub-problems, and solving by an iterative optimization method to obtain suboptimal task segmentation, communication resource allocation and calculation resource allocation solutions; s3: each task device refuses the auxiliary devices which are not allocated with tasks according to the task segmentation result; s4: the unassigned assisting equipment initiates an assisting request to the task equipment again according to the preference of the unassigned assisting equipment to the task equipment to form a new assisting cluster; s5: steps S2-S4 are repeated until the assisting device allocation has stabilized.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a distributed collaborative computing method for mobile crowd sensing.
Background
The mobile crowd sensing utilizes abundant sensors of mobile equipment to collect various sensing data in a large quantity and widely, so as to realize real-time sensing and monitoring of physical environment. However, the mobile device is limited by factors such as size, cost and the like, and the mobile device has limited computing power and cannot bear the processing task of sensing data. At present, aiming at the problem of insufficient computing capacity of mobile crowd sensing equipment, an edge computing or cloud computing solution is adopted, the mobile equipment offloads complex computing tasks to a computing server with stronger computing power, and the computing processing of sensing data is completed in time by means of the powerful computing capacity of the server, so that the real-time performance of the tasks is ensured. The problem of insufficient computing power of the mobile device can be effectively solved by utilizing computing power of the edge computing/cloud computing servers, but the solution depends on infrastructure construction, and the mobile device must be capable of effectively accessing the computing servers to use the computing services provided by the computing servers, so that the solution cannot be suitable for certain scenes, such as performing sensing tasks in remote areas, being too high in load of the computing servers and being inaccessible. In order to solve the problem of insufficient computing capacity of mobile equipment under the condition of limited access, a distributed collaborative computing method for intelligent perception of mobile groups is provided, and the method utilizes computing power of idle mobile equipment to assist in computing perception data and ensures that perception tasks are completed on time.
Disclosure of Invention
In view of the above, the present invention aims to provide a distributed collaborative computing method for mobile crowd sensing, which utilizes computing power of surrounding idle mobile devices to assist in completing complex computing tasks, and assists in device allocation, task segmentation, communication resource allocation and computing resource allocation through joint optimization, so that the whole network weighting energy consumption is minimized under the condition of ensuring the computing time delay constraint of the sensing tasks, and the survival time of the crowd sensing network is maximized.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a distributed collaborative computing method for mobile crowd sensing, comprising the steps of:
s1: the whole network equipment exchanges position and electric quantity information, and the assisting equipment is matched with the task equipment according to the information to complete the allocation of the assisting equipment so as to form an assisting cluster;
s2: in each assistant cluster, decomposing the weighted energy consumption and the minimization problem into two sub-problems, and solving by an iterative optimization method to obtain suboptimal task segmentation, communication resource allocation and calculation resource allocation solutions;
s3: each task device refuses the auxiliary devices which are not allocated with tasks according to the task segmentation result;
s4: the unassigned assisting equipment initiates an assisting request to the task equipment again according to the preference of the unassigned assisting equipment to the task equipment to form a new assisting cluster;
s5: steps S2-S4 are repeated until the assisting device allocation has stabilized.
Further, the step S1 specifically includes:
obtaining the distance between any two devices according to the obtained position informationAccording to the acquired electric quantity information, obtaining the energy consumption weight of any equipment>Thereby obtaining the preference degree of the assisting equipment to the task equipment:
wherein the method comprises the steps of；
The assisting equipment initiates a request to the favorite task equipment according to the calculated preference degree, the task equipment receives the assisting equipment after receiving the request, and after all the assisting equipment sends the request, an assisting cluster taking the task node as a cluster head is formed immediately.
Further, the step S2 specifically includes:
s21: after assisting cluster formation, the problem of minimizing the weighted energy consumption in the cluster is as follows:
s22: problem P1 is broken down into two sub-problems: the task segmentation problem is expressed as:
the joint communication and computing resource optimization problem is expressed as:
s23: calculating an optimal solution of the problem P1.1 through a water-injection-like algorithm, namely selecting equipment with the minimum target cost to distribute tasks until reaching the upper processing limit of the equipment under the condition that the tasks are ensured to be completed on time;
s24: after processing the minutes in the objective function, solving an optimal solution of computing resource allocation by using a dichotomy; solving a suboptimal solution of communication resource allocation by using a Lagrangian dual method according to the characteristic that the dual gap of the problem P1.2 approaches 0; thereby obtaining a solving algorithm of the problem P1.2;
s25: and (3) using an alternate optimization method to alternately optimize the problem P1.1 and the problem P1.2 until the algorithm converges, thereby obtaining a suboptimal solution of the cluster weighted energy consumption minimization problem P1.
Further, in step S23, calculating the optimal solution of the problem P1.1 by using the water-filling algorithm specifically includes: inputting subcarrier allocation resultsTransmission power allocation result->Computing resource allocation results->；
S231: setting a number of task bits to be allocated for each task deviceInitializing device traversal index；
S232: calculating all devices in the current assistance cluster using the following formulaTarget cost
S233: for a pair ofThe results were sorted in ascending order, the result sequence is expressed:>；
s234: if it isThe task segmentation is performed according to the following formula
Wherein n=；
S235: outputting the task segmentation result。
Further, in step S24, the method for solving the optimal solution for computing resource allocation by using the dichotomy specifically includes the following steps: inputting task segmentation resultsLagrangian multiplier->、/>Deviation tolerance->；
Setting a two lower limitSetting a bipartite upper limit +.>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the Setting upAnd calculate +.>The method comprises the steps of carrying out a first treatment on the surface of the If->Setting a bipartite upper limit +.>Otherwise, set the two lower limits +.>The step is iteratively executed until；
Outputting the calculation resource allocation result。
Further, in step S24, the method of solving the sub-optimal solution of the communication resource allocation by using the lagrangian dual method specifically includes the following steps: input split optimization auxiliary variable、/>Maximum number of iterations->Deviation tolerance->；
Setting initialization pull-up Langgy multiplier,/>And->；
Setting initialization iteration timesSetting the result of initializing Lagrangian function +.>；
Obtaining computing resource allocation resultsThe method comprises the steps of carrying out a first treatment on the surface of the For each subcarrier +.>The following calculations were performed: calculation ofWherein->Calculation ofObtaining subcarrier allocation result according to the following formula>And transmission power allocation result->：/>；The method comprises the steps of carrying out a first treatment on the surface of the Updating Lagrangian function result +.>Updating Lagrangian multiplier +.>,/>And->The method comprises the steps of carrying out a first treatment on the surface of the Iteratively executing the step until +.>Or->；
Outputting subcarrier allocation resultsTransmission power allocation result->And calculating a resource allocation result.
Further, the solving algorithm of the problem P1.2 in step S24 is as follows: task segmentation resultsDeviation tolerance->；
Calculation of
Setting initialization iteration timesRandomly generateInitially feasible solution->,/>；
Calculating a split-type optimization auxiliary variable、/>An initial value;
given a givenAnd->Obtain->,/>And->Results; updating the split optimization auxiliary variable +.>And->The method comprises the steps of carrying out a first treatment on the surface of the Iteratively executing the step until +.>And->；
Outputting subcarrier allocation resultsTransmission power allocation result->Computing resource allocation results->。
Further, in step S25, the method of using the alternative optimization performs alternative optimization on the problem P1.1 and the problem P1.2 until the algorithm converges, thereby obtaining a sub-optimal solution of the cluster weighted energy consumption minimization problem P1, which specifically includes the following steps: input assisting device assignment resultsSubcarrier allocation result->Maximum number of iterations->Deviation tolerance;
setting initialization iteration times,/>；
Random generation of initial feasible solutions,/>,/>；
Given a given，/>，/>Obtain->The method comprises the steps of carrying out a first treatment on the surface of the Given->Obtain->，/>，/>The method comprises the steps of carrying out a first treatment on the surface of the Calculating the weighted energy consumption of each assisting cluster>The method comprises the steps of carrying out a first treatment on the surface of the Iteratively executing the step until +.>Or->；
Outputting the task segmentation resultSubcarrier allocation result->Transmission power allocation result->Computing resource allocation results->。
The invention has the beneficial effects that: the invention solves the problem of insufficient computing capacity of the mobile equipment under the condition of limited access, the crowd sensing equipment can unload the computing task which cannot be borne to the adjacent equipment for computing simultaneously in a cooperative computing mode, the weighting energy consumption of the system can be minimized on the premise of completing the sensing task on time by using the proposed joint optimization method, and compared with local computing, the cooperative computing method provided by the invention can greatly improve the task completing rate on time, effectively reduce the energy consumption and expand the survival time of the mobile crowd sensing network.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a system model of a collaborative computing architecture in accordance with the present invention.
Detailed Description
The invention aims to provide a distributed collaborative computing method for mobile crowd sensing, which utilizes the computing power of surrounding idle mobile devices to assist in completing complex computing tasks, and assists in device allocation, task segmentation, communication resource allocation and computing resource allocation through joint optimization, so that the whole network weighting energy consumption is minimized under the condition of ensuring the computing time delay constraint of the sensing tasks, and the survival time of the crowd sensing network is maximized. Aiming at the proposed collaborative computing architecture, the invention constructs a universal system model, which is specifically as follows:
as shown in fig. 1, the system comprisesA single antenna task device->Single antenna assisting device->The bandwidth isIs allocated to the orthogonal subcarriers of the carrier number. Use->Indicating the assigned computing task size for each device,/->Representing the complexity of the computational task +.>Representing the allocated computing resources, whereby the time and energy consumption it takes the device to perform the computing task is available:. Use->Indicating the allocation of sub-carriers, use +.>The distribution condition of the transmission power is represented, and the transmission rate of wireless communication between the devices can be obtained according to the shannon formula:the time and energy consumption it takes for the device to offload computing tasks can thus be derived: />. The weighted energy consumption sum in each co-cluster can be obtained based on the model as follows: />。
The following optimization problems are thus created:
in order to solve the problem, the invention provides a high-efficiency solving method, which specifically comprises the following steps:
step one, the whole network equipment exchanges position and electric quantity information, and the assisting equipment is matched with the task equipment according to the information, so that the assisting equipment is distributed, and an assisting cluster is formed. Specifically, according to the obtained position information, the distance between any two devices can be obtainedAccording to the obtained electric quantity information, the energy consumption weight of any equipment can be obtained>The preference degree of the assisting equipment to the task equipment can be obtained:
wherein->. The assisting equipment initiates a request to the favorite task equipment according to the calculated preference degree, the task equipment receives the assisting equipment after receiving the request, and after all the assisting equipment sends the request, an assisting cluster taking the task node as a cluster head is formed immediately.
And secondly, decomposing the weighted energy consumption and the minimization problem into two sub-problems in each assistant cluster, and solving the sub-problems by an iterative optimization method to obtain suboptimal task segmentation, communication resource allocation and calculation resource allocation solutions. Specifically, after assisting cluster formation, the problem of minimizing the weighted energy consumption in the cluster is obtained as follows:
to solve this problem, the present invention breaks it down into two sub-problems: the task segmentation problem and the joint communication and the computing resource optimization problem. The sub-problem-task segmentation problem is expressed as:
the problem is a linear programming problem, an optimal solution can be obtained through a quasi-water injection algorithm, namely, the equipment with the minimum target cost is selected to distribute tasks until the upper processing limit of the equipment under the condition of ensuring the task to be completed on time is reached, and the algorithm steps are shown in table 1.
TABLE 1
The sub-problem two joint communication and computing resource optimization problem is expressed as:
after the processing of the polynomial terms in the objective function, the optimal solution of computing resource allocation is solved by using a dichotomy, and the specific algorithm flow is shown in table 2.
TABLE 2
By utilizing the characteristic that the dual gap of the problem approaches 0, the sub-optimal solution of the communication resource allocation can be solved by using a Lagrange dual method, and the algorithm steps are shown in table 3.
TABLE 3 Table 3
/>
From this, a solution algorithm for the second sub-problem can be obtained, as shown in Table 4.
TABLE 4 Table 4
And finally, using an alternate optimization method to alternately optimize the first sub-problem and the second sub-problem until the algorithm converges, thereby obtaining a suboptimal solution of the problem of minimizing the weighted energy consumption in the cluster, wherein the algorithm steps are shown in the table 5.
TABLE 5
Step three, each task device refuses the auxiliary devices which are not allocated with tasks according to the task segmentation result;
step four, the unassigned assisting equipment initiates an assisting request to the task equipment again according to the preference of the unassigned assisting equipment to the task equipment to form a new assisting cluster;
and fifthly, repeating the second, third and fourth steps until the auxiliary equipment distribution is stable.
The general algorithm steps are shown in table 6.
TABLE 6
/>
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (7)
1. A distributed collaborative computing method for mobile crowd sensing, characterized by: the method comprises the following steps:
s1: the whole network equipment exchanges position and electric quantity information, and the assisting equipment is matched with the task equipment according to the information to complete the allocation of the assisting equipment so as to form an assisting cluster;
s2: in each assistant cluster, decomposing the weighted energy consumption and the minimization problem into two sub-problems, and solving by an iterative optimization method to obtain suboptimal task segmentation, communication resource allocation and calculation resource allocation solutions;
s3: each task device refuses the auxiliary devices which are not allocated with tasks according to the task segmentation result;
s4: the unassigned assisting equipment initiates an assisting request to the task equipment again according to the preference of the unassigned assisting equipment to the task equipment to form a new assisting cluster;
s5: repeating the steps S2-S4 until the auxiliary equipment distribution is stable;
the step S2 specifically comprises the following steps:
s21: after assisting cluster formation, the problem of minimizing the weighted energy consumption in the cluster is as follows:
s22: problem P1 is broken down into two sub-problems: the task segmentation problem is expressed as:
the joint communication and computing resource optimization problem is expressed as:
s23: calculating an optimal solution of the problem P1.1 through a water-injection-like algorithm, namely selecting equipment with the minimum target cost to distribute tasks until reaching the upper processing limit of the equipment under the condition that the tasks are ensured to be completed on time;
s24: after processing the minutes in the objective function, solving an optimal solution of computing resource allocation by using a dichotomy; solving a suboptimal solution of communication resource allocation by using a Lagrangian dual method according to the characteristic that the dual gap of the problem P1.2 approaches 0; thereby obtaining a solving algorithm of the problem P1.2;
s25: and (3) using an alternate optimization method to alternately optimize the problem P1.1 and the problem P1.2 until the algorithm converges, thereby obtaining a suboptimal solution of the cluster weighted energy consumption minimization problem P1.
2. The distributed collaborative computing method for mobile crowd sensing of claim 1, wherein: the step S1 specifically comprises the following steps:
obtaining the distance between any two devices according to the obtained position informationAccording to the acquired electric quantity information, obtaining the energy consumption weight of any equipment>Thereby obtaining the preference degree of the assisting equipment to the task equipment:
wherein the method comprises the steps of；
The assisting equipment initiates a request to the favorite task equipment according to the calculated preference degree, the task equipment receives the assisting equipment after receiving the request, and after all the assisting equipment sends the request, an assisting cluster taking the task node as a cluster head is formed immediately.
3. The distributed collaborative computing method for mobile crowd sensing of claim 1, wherein: in step S23, calculating an optimal solution of the problem P1.1 by using a water-filling algorithm specifically includes: inputting subcarrier allocation resultsTransmission power allocation result->Computing resource allocation results->；
S231: setting a number of task bits to be allocated for each task deviceInitializing device traversal subscript ++>；
S232: calculating a target cost for all devices in the current assistance cluster using the following formula
S233: for a pair ofThe results were sorted in ascending order, the result sequence is expressed:>；
s234: if it isThe task segmentation is performed according to the following formula
Wherein n=；
S235: outputting the task segmentation result。
4. The distributed collaborative computing method for mobile crowd sensing of claim 1, wherein: the step S24 of solving the optimal solution for computing resource allocation by using a dichotomy specifically includes the following steps: inputting task segmentation resultsLagrangian multiplier->、/>Deviation tolerance->；
Setting a two lower limitSetting a bipartite upper limit +.>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the Setting upAnd calculate +.>The method comprises the steps of carrying out a first treatment on the surface of the If->Setting a bipartite upper limit +.>Otherwise, set the two lower limits +.>The step is iteratively executed until；
Outputting the calculation resource allocation result。
5. The distributed collaborative computing method for mobile crowd sensing of claim 1, wherein: the step S24 of solving the sub-optimal solution of the communication resource allocation by using the lagrangian dual method specifically includes the following steps: input split optimization auxiliary variable、/>Maximum number of iterations->Deviation tolerance->；
Setting initialization pull-up Langgy multiplier, />And->；
Setting initialization iteration timesSetting the result of initializing Lagrangian function +.>；
Obtaining computing resource allocation resultsThe method comprises the steps of carrying out a first treatment on the surface of the For each subcarrier +.>The following calculations were performed: calculation ofWherein->Calculation ofObtaining subcarrier allocation result according to the following formula>And transmission power allocation result->：/>；The method comprises the steps of carrying out a first treatment on the surface of the Updating Lagrangian function result +.>Updating Lagrangian multiplier +.>, />And->The method comprises the steps of carrying out a first treatment on the surface of the Iteratively executing the step until +.>Or->；
Outputting subcarrier allocation resultsTransmission power allocation result->And calculating a resource allocation result.
6. The distributed collaborative computing method for mobile crowd sensing of claim 1, wherein: the solving algorithm of the problem P1.2 in step S24 is as follows: task segmentation resultsDeviation tolerance->；
Calculation of
Setting initialization iteration timesRandomly generating an initial feasible solution->, />；
Calculating a split-type optimization auxiliary variable、/>An initial value;
given a givenAnd->Obtain->, />And->Results; updating the split optimization auxiliary variable +.>Andthe method comprises the steps of carrying out a first treatment on the surface of the Iteratively executing the step until +.>And->；
Outputting subcarrier allocation resultsTransmission power allocation result->Computing resource allocation results->。
7. The distributed collaborative computing method for mobile crowd sensing of claim 1, wherein: the method of using alternative optimization in step S25 performs alternative optimization on the problem P1.1 and the problem P1.2 until the algorithm converges, thereby obtaining a suboptimal solution of the cluster weighted energy consumption minimization problem P1, which specifically includes the following steps: input assisting device assignment resultsSubcarrier allocation result->Maximum number of iterations->Deviation tolerance;
setting initialization iteration times,/>；
Random generation of initial feasible solutions, />,/>；
Given a given，/>，/>Obtain->The method comprises the steps of carrying out a first treatment on the surface of the Given->Obtain->，/>，/>The method comprises the steps of carrying out a first treatment on the surface of the Calculating the weighted energy consumption of each assisting cluster>The method comprises the steps of carrying out a first treatment on the surface of the Iteratively executing the step until +.>Or->；
Outputting the task segmentation resultSubcarrier allocation result->Transmission power allocation result->Computing resource allocation results。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310753432.9A CN116506877B (en) | 2023-06-26 | 2023-06-26 | Distributed collaborative computing method for mobile crowd sensing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310753432.9A CN116506877B (en) | 2023-06-26 | 2023-06-26 | Distributed collaborative computing method for mobile crowd sensing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116506877A CN116506877A (en) | 2023-07-28 |
| CN116506877B true CN116506877B (en) | 2023-09-26 |
Family
ID=87330505
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310753432.9A Active CN116506877B (en) | 2023-06-26 | 2023-06-26 | Distributed collaborative computing method for mobile crowd sensing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116506877B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110505644A (en) * | 2019-09-26 | 2019-11-26 | 江南大学 | User task unloading and resource allocation joint optimization method under 5G super-intensive heterogeneous network |
| CN112105062A (en) * | 2020-09-16 | 2020-12-18 | 云南大学 | Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition |
| CN112689303A (en) * | 2020-12-28 | 2021-04-20 | 西安电子科技大学 | Edge cloud cooperative resource joint allocation method, system and application |
| CN113904947A (en) * | 2021-11-15 | 2022-01-07 | 湖南大学无锡智能控制研究院 | Vehicle-road cooperative distributed edge computing task unloading and resource allocation method and system |
| CN114827191A (en) * | 2022-03-15 | 2022-07-29 | 华南理工大学 | Dynamic task unloading method for fusing NOMA in vehicle-road cooperative system |
| CN114885418A (en) * | 2022-03-17 | 2022-08-09 | 南京邮电大学 | Joint optimization method, device and medium for task unloading and resource allocation in 5G ultra-dense network |
| CN115665860A (en) * | 2022-10-28 | 2023-01-31 | 北京航空航天大学 | Unmanned aerial vehicle ad hoc network resource allocation method based on migratory bird swarm characteristics |
-
2023
- 2023-06-26 CN CN202310753432.9A patent/CN116506877B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110505644A (en) * | 2019-09-26 | 2019-11-26 | 江南大学 | User task unloading and resource allocation joint optimization method under 5G super-intensive heterogeneous network |
| CN112105062A (en) * | 2020-09-16 | 2020-12-18 | 云南大学 | Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition |
| CN112689303A (en) * | 2020-12-28 | 2021-04-20 | 西安电子科技大学 | Edge cloud cooperative resource joint allocation method, system and application |
| CN113904947A (en) * | 2021-11-15 | 2022-01-07 | 湖南大学无锡智能控制研究院 | Vehicle-road cooperative distributed edge computing task unloading and resource allocation method and system |
| CN114827191A (en) * | 2022-03-15 | 2022-07-29 | 华南理工大学 | Dynamic task unloading method for fusing NOMA in vehicle-road cooperative system |
| CN114885418A (en) * | 2022-03-17 | 2022-08-09 | 南京邮电大学 | Joint optimization method, device and medium for task unloading and resource allocation in 5G ultra-dense network |
| CN115665860A (en) * | 2022-10-28 | 2023-01-31 | 北京航空航天大学 | Unmanned aerial vehicle ad hoc network resource allocation method based on migratory bird swarm characteristics |
Non-Patent Citations (3)
| Title |
|---|
| Community Detecting Oriented Directed and Weighted Network in Mobile Crowd Sensing;Jian An等;2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom);全文 * |
| 基于移动边缘计算的智能电网传输与计算时延优化研究;巩龙豪;中国优秀硕士学位论文全文数据库工程科技Ⅱ辑;全文 * |
| 移动群智感知中面向用户区域的分布式多任务分配方法;韩俊樱等;计算机应用;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116506877A (en) | 2023-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111586720B (en) | Task unloading and resource allocation combined optimization method in multi-cell scene | |
| Wu et al. | Split learning over wireless networks: Parallel design and resource management | |
| CN107766135B (en) | Task allocation method based on particle swarm optimization and simulated annealing optimization in moving cloud | |
| CN112105062B (en) | Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition | |
| CN112000481B (en) | Task unloading method for maximizing computing capacity of D2D-MEC system | |
| CN110233755B (en) | Computing resource and frequency spectrum resource allocation method for fog computing in Internet of things | |
| CN113286317B (en) | Task scheduling method based on wireless energy supply edge network | |
| CN113596785B (en) | D2D-NOMA communication system resource allocation method based on deep Q network | |
| CN115802389B (en) | Federal learning method trained by digital twin auxiliary model | |
| CN114745383A (en) | Mobile edge calculation assisted multilayer federal learning method | |
| CN116156563A (en) | Heterogeneous task and resource end edge collaborative scheduling method based on digital twin | |
| Xu et al. | Distributed assignment with load balancing for dnn inference at the edge | |
| CN116506877B (en) | Distributed collaborative computing method for mobile crowd sensing | |
| CN107426775B (en) | Distributed multi-user access method for high-energy-efficiency heterogeneous network | |
| Ullah et al. | Ego network-based virtual network embedding scheme for revenue maximization | |
| Zhao et al. | Convergence study of decentralized min-cost subgraph algorithms for multicast in coded networks | |
| CN110602718A (en) | Heterogeneous cellular network power distribution method and system based on alternative direction multiplier method | |
| CN115987375A (en) | Power equipment association and resource optimization method, system and medium for converged network | |
| CN113157344B (en) | DRL-based energy consumption perception task unloading method in mobile edge computing environment | |
| CN114640966A (en) | Task unloading method based on mobile edge calculation in Internet of vehicles | |
| CN112822702B (en) | Joint task unloading and resource allocation method in master-slave MEC network | |
| Yao et al. | Multi-objective cooperative computation offloading for mec in uavs hybrid networks via integrated optimization framework | |
| CN113590335B (en) | Task load balancing method based on grouping and delay estimation in tree edge network | |
| CN117094381B (en) | Multi-mode federal collaboration method taking high-efficiency communication and individuation into consideration | |
| CN116582893A (en) | Neural network model segmentation and resource allocation method in wireless energy supply edge intelligent scene |
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 |