CN115843070B - Ocean sensing network calculation unloading method and system based on energy collection technology - Google Patents
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Abstract
The invention discloses a calculation unloading method and a calculation unloading system for a marine sensing network based on an energy collection technology, which relate to the technical field of marine observation sensing networks.
Description
Technical Field
The invention relates to the technical field of ocean observation sensing networks, in particular to an ocean sensing network calculation unloading method and system based on an energy collection technology.
Background
The traditional marine observation is mainly sea-based observation based on investigation ships and submerged buoys or space-based observation based on satellite remote sensing and aviation observation. Due to the complexity and uniqueness of the marine environment, the development of marine science is restricted by the problems of short time, discontinuous marine observation data and the like. The submarine observation network derived from the united states navy underwater sound monitoring system during the cold fight period is a third ocean scientific observation platform established by humans. Under the promotion of novel technologies such as modern sensors, underwater robots, submarine optical fiber cables, internet of things and big data, the submarine observation network integrates subjects such as physical ocean, ocean chemistry, ocean geophysics and ocean ecology, solves the technical problem of obtaining the ocean observation data in high resolution and real time in deep sea and extreme environments, can go deep into the ocean to observe and know the ocean, and realizes all-weather, long-term, continuous, comprehensive, real-time and in-situ observation from the sea bottom to the sea surface.
The establishment of a distributed, networked, interactive and comprehensive intelligent three-dimensional observation network is a development trend of ocean science observation. With the application of the internet of things in the ocean field, the observation stations, the observation nodes, the satellite remote sensing, the unmanned surface vessels and other observation means scattered at all positions are integrated through unified and universal data standards to perform cooperative work, so that a layering, comprehensive and intelligent air-sky-ocean integrated stereoscopic observation network covering the offshore, regional and global sea areas is formed. Because of the limited resources (e.g., computing and energy capacity) of the sensing device, computing power can be greatly reduced when energy is low, and even business failure can result. The computing task in the sensing equipment is unloaded to the remote cloud through the sensing network gateway, so that the computing problem can be effectively solved. However, cloud processing fails to meet the stringent delay requirements of many observation applications that typically require real-time processing and response (e.g., disaster response).
The fog node is an essential element of the fog architecture, and the fog node may be any device that provides computing, networking, storage, and acceleration elements of the fog architecture, such as industrial controllers, switches, routers, embedded servers, complex gateways, programmable logic controllers (PLC, programmableLogic controllers), and intelligent internet of things nodes (e.g., video surveillance cameras), etc. Considering that the fog node has the functions of calculating and storing network resources, the service performance of the ocean observation network can be effectively improved by deploying the fog node near the ocean sensing equipment.
The fog node is generally connected to the sensor network gateway and is used for processing the calculation tasks from the sensor network equipment so as to provide instant service response; the energy harvesting technique can harvest a small amount of unconventional energy readily available in the environment and convert it into electrical energy, continuously powering the sensing device. However, in marine environments with complex environments and dynamically changed topology, the traditional task allocation method cannot be effectively applied, and due to the requirement of quality of service (QoS, quality of Service), the traditional unloading method either unloads all computing tasks to the nearest fog node, which can cause overload of some fog nodes and insufficient load of sensing equipment; or the sensing device bears the calculation task, so that the task failure is caused by the rapid electric quantity reduction of the sensing device. Therefore, the existing task allocation/offloading method cannot be applied to the ocean sensing network to which the energy collection technology is applied, aiming at the ocean environment in which the nodes dynamically change.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a system for calculating and unloading a marine sensing network based on an energy collection technology, which are suitable for the marine sensing network applying the energy collection technology, provide an optimal calculating and unloading strategy, determine a proper task unloading proportion for marine sensing equipment and fog nodes, improve the calculation performance of the marine sensing network and avoid node resource waste.
In a first aspect, the present disclosure provides a method for computing and offloading a marine sensing network based on an energy harvesting technique.
An ocean sensing network calculation unloading method based on an energy collection technology comprises the following steps:
acquiring real-time information of ocean sensing equipment in a current time slot in each time slot; the real-time information comprises a task request indicated value of a calculation task requested by the marine sensing equipment, channel power of the marine sensing equipment for accessing the fog node and battery energy level of the marine sensing equipment;
constructing a calculation delay and energy consumption of the marine sensing device for executing the calculation task in the current time slot, and a transmission delay and energy consumption for unloading the calculation task to the fog node;
constructing a calculation task execution cost model, an ocean sensing equipment energy consumption model and an energy collection model, taking the minimum long-term average execution cost as an objective function, and constructing a calculation unloading model by combining the constructed models;
and according to the acquired real-time information, solving a calculation unloading model by using a dynamic algorithm, and solving to acquire an optimal calculation unloading decision of the current time slot.
Further technical proposal, based on the scheduled frequency of W CPU cycles required by the ocean sensing equipment to complete the calculation task at the t time slot
Constructing a computation delay for a marine sensing device to perform a computation task locally in a t-th time slot
And energy consumption->
The formula is:
wherein ,kin order to be able to switch the capacitance in an efficient way,wthe number of CPU cycles is represented by w=1, …, W.
Further technical scheme, based on channel power of access fog node of ocean sensing equipment
And transmit power->
According to shannon formula, constructing transmission delay of ocean sensing equipment for unloading calculation task to fog node in t-th time slot
And energy consumption->
The formula is:
wherein L represents the input data amount of the calculation task, r represents the transmission rate of the t-th time slot, and
in the above equation, ω denotes a channel bandwidth, and σ denotes gaussian noise power inside the channel.
According to a further technical scheme, the weighted sum of the execution delay and the task discarding cost is taken as the calculation task execution cost, and the calculation task execution cost model is as follows:
wherein ,
computing task execution cost representing the t-th slot, < >>
Indicating penalty not performed after acquisition of the computational task,/->
Representing marine transmissionThe sensing device performs the calculation delay of the calculation task locally in the t-th time slot,/for the calculation task>
Representing the transmission delay of the marine sensing device for offloading the computation task to the mist node in the t-th time slot, a +.>
=1 and
=1 indicates that at time slot t, the requested computation task is performed on the marine sensor device and the computation task is offloaded to the fog node, respectively.
Further technical proposal, in each time slot, part of energy reaching the ocean sensing equipment based on the energy collecting technology is recorded as
An energy collection model is constructed as follows:
wherein ,
representing the energy reaching the marine sensing device at the beginning of the t-th time slot.
According to a further technical scheme, the ocean sensing equipment energy consumption model is energy consumed by the ocean sensing equipment in the t-th time slot, and the formula is as follows:
and satisfies the energy relationship constraint:
wherein ,
representing the battery energy level of the marine sensing device at time t, +.>
Index representing computational offload model, +.>
=1 and
=1 means that at time slot t, the requested calculation task is performed on the marine sensor device and the calculation task is offloaded to the mist node, +.>
A predetermined frequency representing the CPU period at the current t-th slot,/or->
Indicating the transmit power of the marine sensing device accessing the mist node,/->
Energy consumption representing the local execution of a computational task by a marine sensor device in the t-th time slot, a->
Representing the energy consumption of the marine sensing device to offload a computing task to a fog node in the t-th time slot.
According to a further technical scheme, the calculation unloading model is as follows:
s.t.
wherein ,
indicating maximum transmission power at the t-th slot,/->
Predetermined frequency representing maximum CPU period at the t-th time slot,/or->
Representing the computational task execution cost of the T-th time slot, t=0, 1,..>
Representing the battery energy level of the marine sensing device at the current t-th time slot,/for the marine sensing device>
Index representing computational offload model, +.>
=1 and
=1 means that at time slot t, the requested calculation task is performed on the marine sensor device and the calculation task is offloaded to the mist node, +.>
A predetermined frequency representing the CPU period at the current t-th slot,/or->
Indicating the transmit power of the marine sensing device accessing the mist node,/->
Representing part of the energy reaching the marine sensing device, < +.>
Representing the predetermined frequency of W CPU cycles required by the marine sensing device to complete the computing task at time t.
According to a further technical scheme, the optimal calculation unloading decision in the current time slot is solved by utilizing a dynamic algorithm, and the method comprises the following steps:
constructing a Lyapunov function;
constructing a Lyapunov drift function and a Lyapunov drift plus penalty function according to the Lyapunov function;
simplifying and calculating an unloading model according to the Lyapunov drift function and the Lyapunov drift plus penalty function;
and solving the simplified calculation unloading model to obtain an optimal calculation unloading decision in the current time slot.
Further technical solutions, the solving includes:
when the t time slot starts, acquiring a task request instruction value of a calculation task requested by the ocean sensing equipment in the current time slot
Battery energy level of marine sensor device +.>
And channel power of marine sensing device access fog node +.>
；
According toThe obtained information of the current time slot is substituted into the simplified calculation unloading model to be solved, and the index of the calculation unloading model of the current time slot is determined
Predetermined frequency of CPU cycle->
Transmit power of marine sensing device access fog node +.>
Partial energy reaching the marine sensor system based on energy harvesting technology>
；
And updating a battery energy queue of the ocean sensing equipment according to the result of calculation determination, judging whether the battery energy queue reaches a stable value of the current time slot, if so, outputting the result of calculation determination as an optimal calculation unloading decision in the current time slot, and if not, calculating an optimal calculation unloading decision of the next time slot.
In a second aspect, the present disclosure provides a marine sensing network computing offloading system based on an energy harvesting technique.
An energy harvesting technology-based marine sensing network computing and offloading system, comprising:
the information acquisition module is used for acquiring real-time information of the marine sensing equipment in the current time slot in each time slot, wherein the real-time information comprises a task request indication value of a calculation task requested by the marine sensing equipment, channel power of the marine sensing equipment for accessing the fog node and battery energy level of the marine sensing equipment;
the model construction module is used for constructing calculation delay and energy consumption of a calculation task executed by the marine sensing equipment in the current time slot, transmission delay and energy consumption of unloading the calculation task to the fog node, constructing a calculation task execution cost model, a marine sensing equipment energy consumption model and an energy collection model, taking the minimum long-term average execution cost as an objective function, and constructing a calculation unloading model by combining the constructed model;
and the unloading decision solving module is used for solving the calculation unloading model by utilizing a dynamic algorithm according to the acquired real-time information, and solving to acquire the optimal calculation unloading decision of the current time slot.
The one or more of the above technical solutions have the following beneficial effects:
1. the invention provides a calculation unloading method and a calculation unloading system for a marine sensing network based on an energy collection technology.
2. The invention considers the problem that the ocean sensing equipment has limited resources and can not meet the memory requirement of the storage optimal strategy, and provides a dynamic algorithm for solving, wherein a Lyapunov drift function and a Lyapunov drift and penalty function are utilized to simplify a calculation unloading model, iterative solution is carried out according to the acquired real-time information, the current optimal calculation unloading decision is acquired, so that the resource allocation is carried out, the resource allocation result is optimized, the long-term average execution cost is reduced, and the user service quality of the system is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a method for computing and offloading a marine sensing network based on an energy harvesting technique according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a computational offload model in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of energy harvesting in an embodiment of the invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
The embodiment provides a method for calculating and unloading a marine sensor network based on an energy collection technology, which is shown in fig. 1 and comprises the following steps:
step S1, acquiring real-time information of marine sensing equipment in a current time slot in each time slot, wherein the real-time information comprises a task request indication value of a calculation task requested by the marine sensing equipment, channel power of the marine sensing equipment for accessing a fog node and battery energy level of the marine sensing equipment;
step S2, constructing calculation delay and energy consumption of a calculation task executed by the marine sensing equipment in the current time slot, and unloading the calculation task to transmission delay and energy consumption of a fog node;
s3, constructing a calculation task execution cost model, an ocean sensing equipment energy consumption model and an energy collection model, and constructing a calculation unloading model by taking the minimum long-term average execution cost as an objective function and combining the constructed model;
and S4, solving a calculation unloading model by using a dynamic algorithm according to the acquired real-time information, and solving to obtain an optimal calculation unloading decision of the current time slot, wherein the calculation unloading decision is used for representing the execution mode of the calculation task requested by the ocean sensing equipment.
In this embodiment, a calculation unloading model is considered, which is formed by a plurality of ocean sensing devices with calculation capability and a fog node, as shown in fig. 2, where the fog node is located d meters away from the ocean sensing devices and is in communication connection with an ocean observational network, and the ocean sensing devices (such as the ocean sensing devices A, B, C shown in fig. 2) can be ships, coastal base stations, and the like, and can access the fog node through a wireless channel. The marine sensing device associates the node server and runs a virtual machine, performs a computational task on behalf of the sensing device, and by transferring part of the computational task to the node for execution, quality of service QoS may be significantly improved.
In the step S1, the real-time information of the ocean sensing device in the current time slot is obtained. The real-time information comprises a task request indication value of a calculation task requested by the marine sensing equipment
Channel power of ocean sensing device access fog node +.>
And battery energy level of the marine sensing device +.>
. And according to the acquired information, solving the constructed calculation unloading model, and solving to acquire the optimal calculation unloading decision in each time slot.
In the present embodiment, the time is sliced into a plurality of time slots (or time slices), each time slot having a length of
The radio channel remains static within each time slot, but varies between different time slots, labeled Γ e {0, 1. With A (L,)>
) Represents a calculation task, wherein L (unit is bit) represents the data size of the calculation task, and +.>
Representing the time until completion of the calculation task, i.e. calculation task A (L, ++>
) Need to be in the time +.>
And (5) finishing the process. The marine sensing device runs an application program whose requested computational task is modeled as a Bernoulli process. At the beginning of each time slot, task a (L,
) The probability of ρ is requested, and correspondingly, the probability of (1- ρ) is not requested. When the calculation task is requested in the t-th time slot, let the task request instruction value +.>
=1, otherwise->
=0, i.e. P (->
=1)=1-P(
=0)=ρ，t∈Γ，P(
=1) =ρ represents the probability of ρ, P (++) when the calculation task is requested>
=0) =1- ρ represents the probability of 1- ρ when the calculation task is not requested. In this embodiment, no buffers are available for computing request queuing and applications running on the marine sensor deviceDelay-sensitive applications for which the task execution time is not longer than the slot length, i.e. +.>
。
Obtaining channel power of access fog node of ocean sensing equipment in t time slot
, wherein ,
∽
(x) And t is Γ, < ->
(x) Is->
Is a cumulative distribution function (CDF, cumulativedistribution function).
After the real-time information of the marine sensing device is obtained, step S2 is executed, and according to the obtained real-time information of the marine sensing device, the calculation delay and the energy consumption of the marine sensing device for executing the calculation task in the time slot are constructed, and the transmission delay and the energy consumption of the marine sensing device for unloading the calculation task to the fog node in the time slot are constructed.
In particular, each computing task may be performed locally on the marine sensing device or off-load onto the fog node for execution thereby. Order the
E {0,1}, j= { m, s }, will +.>
As an index to calculate the offload model, wherein +.>
=1 and
=1 then indicates that at the t-th time slot, the requested calculation task is performed or offloaded on the marine sensor device to the fog node, in particular, when
When=1, there is ∈>
=0, indicating that at time slot t, the requested computing task is performed on the marine sensing device; when->
When=1, there is ∈>
=0, meaning that at time slot t, the requested calculation task is offloaded to the foggy node for execution. Therefore, the computational offload model should satisfy:
the marine sensing device requires a predetermined frequency of multiple CPU cycles to accomplish this computational task. The number of CPU cycles required to process 1 bit-sized input data is noted as X, which can be obtained by off-line measurement from different applications, to complete the calculation task a (L,
) W=lx CPU cycles are required. W CPU cycles are needed for completing the calculation task in the t time slot, and the preset frequency of the W CPU cycles in the t time slot is recorded as +.>
I.e. one time slot corresponds to a predetermined frequency, which can be achieved by adjusting the chip voltage by means of DVFS techniques.
Predetermined frequency based on W CPU cycles required by the marine sensing device to complete the computing task at time t
Construction of a computation delay of a marine sensor device for performing a computation task at time t>
The method comprises the following steps:
accordingly, constructing the energy consumption of the marine sensing device to perform the computational tasks locally at time t
The method comprises the following steps:
where k is the effective switched capacitance, the value of which depends on the chip architecture.
In order to reduce the amount of computation upon mobile execution, the computing task a (L,
) And unloading to the fog node. In this embodiment, it is assumed that the amount of input data of the calculation task is small, and thus the transmission delay of the feedback is also negligible. Channel power for accessing fog node based on ocean sensing equipment>
And transmit power->
According to shannon formula, constructing transmission delay +.f for the marine sensing device to offload computing task to fog node in the t-th time slot>
The method comprises the following steps:
where L represents the amount of input data of the computing task, r represents the transmission rate of the t-th slot, and:
in the above equation, ω denotes a channel bandwidth, and σ denotes gaussian noise power inside the channel.
Accordingly, constructing the marine sensing device offloads the computing task to the energy consumption of the fog node in the t-th time slot
The method comprises the following steps:
in the step S3, on the basis of obtaining the time delay and the energy consumption of the marine sensing device for executing the calculation task under different conditions through the step S2, a calculation task execution cost model, a marine sensing device energy consumption model and an energy collection model are first constructed.
In devices that apply energy harvesting techniques, the design of computational offload strategies is more complex than traditional mobile cloud computing systems, and battery energy levels are time dependent, such that system decisions are coupled at different time slots. Therefore, an optimal computational offload policy should strike a good balance between the computational performance of the current and future computational tasks.
Task execution delay is one of the key indicators of user QoS (Quality ofService ), which is used by the present embodiment to calculate the offloading policy. Because of the intermittent and sporadic nature of the collected energy, some requested computing tasks may not be performed and may have to be abandoned, for example because of insufficient local computing energy, while the wireless path from the marine sensing device to the fog node is in deep fade, i.e., the task cannot be offloaded. With this in mind, each abandoned computing task is penalized in a cost unit. Thus, the present embodiment builds a computational task execution cost model, defining the execution cost as a weighted sum of the execution delay and the task discard cost, which can be expressed by the following equation:
wherein ,
representing a penalty that is not performed after the computational task is obtained, in fact, the penalty also pertains to the cost of task execution.
As shown in fig. 3, the energy harvesting process may be considered as continuous energy packets arriving at the marine sensing device, in particular, wind energy, tidal energy, solar energy is converted to electrical energy by transducers and conversion circuitry, and electrical energy is stored directly or indirectly through power management circuitry into an energy storage device (such as the marine sensing device described in this embodiment). Energy reaching the marine sensing device at the beginning of the t-th time slot by energy harvesting
Wherein +.>
Maximum value of +.>
. In each time slot, the part of the energy arriving at the marine sensing device based on the energy harvesting technique is recorded as +.>
An energy collection model is constructed as follows:
wherein ,
，
for the maximum of the energy reached in all time slots.
The energy is collected and stored in the battery of the marine sensing device and, in the next time slot, can be used for local calculation or for calculation offloading.
Furthermore, an energy consumption model of the ocean sensing equipment is built, namely the energy consumed by the ocean sensing equipment in the t-th time slot is as follows:
while satisfying the following energy relationship constraints:
wherein ,Brepresenting the battery energy level of the marine sensing device,
representing the battery power level of the ocean sensing device for the t-th time slot (i.e., the current time slot).
Thus, the energy level change of a marine sensing device battery can be described as:
and then, constructing a calculation unloading model by taking the minimum long-term average execution cost as an objective function and combining the constructed model.
In particular, the problem is to be optimized
The method is described as taking the minimum long-term average execution cost as an objective function, constructing a calculation unloading model, and comprises the following steps of:
s.t.
in the above-mentioned method, the step of,
represents the maximum transmission power (i.e. maximum transmission power) at the t-th slot, where->
The maximum CPU predetermined frequency at the T-th slot is indicated, T indicates the total number of slots, t=0, 1.
After the calculation unloading model is built, step S4 is executed, namely, the calculation unloading model is solved by utilizing a dynamic algorithm according to the acquired real-time information, and the optimal calculation unloading decision of the current time slot is obtained.
In the offloading system considered, the system state consists of task requests, collectable energy, battery energy levels and channel states, the actions are energy collection and computation offloading decisions, including CPU cycle scheduling frequency and allocated transmit power. The operation depends only on the current system state. In addition, due toThe long-term average execution cost is minimized as an objective function, and thus, the problem
In effect a markov decision Process (MDP, markovDecision Process) problem. In principle, problem->
The optimal solution can be obtained through a standard MDP algorithm, such as a relative value iterative algorithm and a linear programming re-formulation method. However, both algorithms require the use of finite states to describe the system and discrete feasible sets of actions. Since the MDP algorithm is based on numerical iterations, it is difficult to obtain a solution. In addition, the quantization status and the set of possible actions may lead to serious performance degradation, and the marine sensing device resources are very limited and cannot meet the memory requirement of storing the optimal policy. Therefore, the present embodiment proposes a dynamic solving algorithm to solve the problem->
。
The computational offload decision (i.e., the solution of the computational offload model) can be described at a time node by a multidimensional vector, and the Lyapunov function is a non-negative, scalar representation of the state represented by the majority vector. If the system is developing towards an unexpected direction, the Lyapunov function will become larger, so that the Lyapunov function approaches to 0 along the negative direction of the x axis to make the Lyapunov function tend to be stable, and when the Lyapunov stability is reached, the calculation unloading decision (i.e. the solution formed by the multidimensional vector) at this time is considered to be the optimal decision of the current time node.
In this embodiment, first, based on the battery energy level of the ocean sensing device of the current time slot, a lyapunov function is constructed, specifically as follows:
then, a Lyapunov drift function is constructed, specifically as follows:
introducing expectations in the Lyapunov drift function, the introduced expectations are shown as follows:
in the above-mentioned method, the step of,
indicating a desire.
Naturally, the process is carried out,
where C is a bounded constant greater than 0.
Due to solving the problem
(i.e., calculating the objective function of the offload model) to obtain an optimal solution, which is computationally complex and difficult to calculate, thus properly amplifying the problem by the above-described manner +.>
By solving the problem->
Get questions->
Is a set of optimal solutions. Problems formed->
The method comprises the following steps:
s.t.
in the question of
And (3) simplifying again on the basis of the above, namely accumulating Lyapunov drift functions of all time slots to obtain:
in the above-mentioned method, the step of,
representing the battery energy level of the marine sensing device at time slot 0, i.e. the initial battery energy level of the marine sensing device, is typically set +.>
=0；
Representing the battery power level of the marine sensing device at the T-th time slot after T time slots (denoted as 0~T-1 total T time slots) have elapsed.
Taking expectations on both sides of the above equation, we get:
to establish the above, let
Then a question->
The method comprises the following steps:
s.t.
thus, the lyapunov drift function and the lyapunov drift plus penalty function can be expressed as:
the minimum value is found in each time slot by means of weightVTo adjust the pair
and
Is important, thus creating a new problem->
The method comprises the following steps:
s.t.
through the scheme, the problem that the optimal solution is difficult to solve is solved by utilizing the Lyapunov drift function and the Lyapunov drift plus penalty function to simplify the calculation unloading model
Conversion value problem->
By solving the problem->
And obtaining a solution set which is almost different from the optimal solution and contains the optimal solution.
After the simplification of the computational offload model is completed, the problem is solved by the following algorithm
I.e., solving the optimal computational offload decisions in the current time slot.
Firstly, when the t time slot starts, acquiring a task request indicating value of a calculation task requested by the ocean sensing equipment in the current time slot
(task request indication value>
The value of {0,1}, representing whether a computational task was acquired), the battery energy level of the marine sensing device +.>
And channel power of marine sensing device access fog node +.>
。
Secondly, substituting the obtained information of the current time slot into an objective function of the simplified calculation unloading model to solve to obtain a solution consisting of multidimensional vectors, namely determining the index of the calculation unloading model
Predetermined frequency of CPU cycle->
Transmit power of marine sensing device access fog node +.>
Partial energy reaching the marine sensor system based on energy harvesting technology>
. The simplified calculation unloading model is shown as follows:
s.t.
thereafter, the battery energy queue of the marine sensing device is updated based on the result of the calculation determination, i.e. updated
Judging whether the stable value of the current time slot is reached or not, namely judging whether the constraint condition of the calculation unloading model is met or not, if so, considering that the stable value of the current time slot is reached, and outputting the result of calculation determination as the optimal calculation unloading decision in the current time slot; otherwise, calculating the optimal calculation unloading decision of the next time slot.
Considering the limitation of the resources (calculation, storage and energy) of the ocean sensing equipment and the dynamic topology of the ocean nodes, the traditional unloading method cannot be applied, so that the embodiment provides a dynamic calculation unloading strategy based on Lyapunov (Lyapunov) optimization, the strategy is low in complexity, the weak calculation capacity of the ocean nodes is adapted, the energy and the calculation capacity of each ocean node are fully utilized, the calculation tasks are more reasonably distributed, the purposes of reducing average decision time delay and system energy consumption are achieved, and the quality of service QoS is ensured.
Example two
The embodiment provides a marine sensor network computing and unloading system based on an energy collection technology, which comprises the following components:
the information acquisition module is used for acquiring real-time information of the marine sensing equipment in the current time slot in each time slot, wherein the real-time information comprises a task request indication value of a calculation task requested by the marine sensing equipment, channel power of the marine sensing equipment for accessing the fog node and battery energy level of the marine sensing equipment;
the model construction module is used for constructing calculation delay and energy consumption of a calculation task executed by the marine sensing equipment in the current time slot, transmission delay and energy consumption of unloading the calculation task to the fog node, constructing a calculation task execution cost model, a marine sensing equipment energy consumption model and an energy collection model, taking the minimum long-term average execution cost as an objective function, and constructing a calculation unloading model by combining the constructed model;
and the unloading decision solving module is used for solving the calculation unloading model by utilizing a dynamic algorithm according to the acquired real-time information, and solving to acquire the optimal calculation unloading decision of the current time slot.
The steps involved in the second embodiment correspond to those of the first embodiment of the method, and the detailed description of the second embodiment can be found in the related description section of the first embodiment.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (5)
1. The ocean sensing network calculation unloading method based on the energy collection technology is characterized by comprising the following steps of:
acquiring real-time information of ocean sensing equipment in a current time slot in each time slot; the real-time information comprises a task request indicated value of a calculation task requested by the marine sensing equipment, channel power of the marine sensing equipment for accessing the fog node and battery energy level of the marine sensing equipment;
constructing a calculation delay and energy consumption of the marine sensing device for executing the calculation task in the current time slot, and a transmission delay and energy consumption for unloading the calculation task to the fog node;
constructing a calculation task execution cost model, an ocean sensing equipment energy consumption model and an energy collection model, taking the minimum long-term average execution cost as an objective function, and constructing a calculation unloading model by combining the constructed models;
according to the acquired real-time information, a dynamic algorithm is utilized to solve a calculation unloading model, and an optimal calculation unloading decision of the current time slot is obtained;
the computational offload model is:
s.t.
wherein ,
，
computing task execution cost representing the t-th slot, < >>
Indicating penalty not performed after acquisition of the computational task,/->
A calculation delay representing the local execution of a calculation task by the marine sensor device in the t-th time slot, a->
Representing the transmission delay of the marine sensing device for offloading the computation task to the mist node in the t-th time slot, a +.>
Index representing computational offload model, +.>
=1 and
=1 means that at time slot t, the requested computation task is performed on the marine sensing device and the computation task is offloaded to the fog node;
solving an optimal computational offload decision in a current time slot using a dynamic algorithm, comprising:
constructing a Lyapunov function;
constructing a Lyapunov drift function and a Lyapunov drift plus penalty function according to the Lyapunov function;
simplifying and calculating an unloading model according to the Lyapunov drift function and the Lyapunov drift plus penalty function;
solving the simplified calculation unloading model to obtain an optimal calculation unloading decision in the current time slot;
the solving includes:
when the t time slot starts, acquiring a task request instruction value of a calculation task requested by the ocean sensing equipment in the current time slot
Battery energy level of marine sensor device +.>
And channel power of marine sensing device access fog node +.>
；
Substituting the information of the current time slot into the simplified calculation unloading model to solve, and determining the index of the calculation unloading model of the current time slot
Predetermined frequency of CPU cycle->
Transmitting power of ocean sensing equipment for accessing fog node
Partial energy reaching the marine sensor system based on energy harvesting technology>
；
And updating a battery energy queue of the ocean sensing equipment according to the result of calculation determination, judging whether a stable value of the current time slot is reached, namely judging whether constraint conditions of a calculation unloading model are met, if so, outputting the result of calculation determination as an optimal calculation unloading decision in the current time slot, otherwise, calculating an optimal calculation unloading decision of the next time slot.
2. The energy harvesting technique-based ocean sensing network computing offload of claim 1, wherein the predetermined frequency based on w CPU cycles required by the ocean sensing device to complete the computing task at the t-th time slot
Constructing a computation delay for the marine sensor device to perform the computation task locally in the t-th time slot +.>
And energy consumption->
The formula is:
wherein ,kin order to be able to switch the capacitance in an efficient way,wthe number of CPU cycles is represented by w=1, …, W.
3. The energy harvesting technique-based ocean sensing network computing offloading method of claim 1, wherein the channel power of the mist node is accessed based on ocean sensing equipment
And transmit power->
According to shannon formula, constructing transmission delay +.f for the marine sensing device to offload computing task to fog node in the t-th time slot>
And energy consumption->
The formula is:
wherein L represents the input data amount of the calculation task, r represents the transmission rate of the t-th time slot, and
in the above equation, ω denotes a channel bandwidth, and σ denotes gaussian noise power inside the channel.
4. The energy harvesting technique-based ocean sensing network computing offload method of claim 1, wherein the fraction of energy arriving at the ocean sensing apparatus during each time slot based on the energy harvesting technique is recorded as
An energy collection model is constructed as follows:
wherein ,
representing the energy reaching the marine sensing device at the beginning of the t-th time slot.
5. An ocean sensing network computing and unloading system based on an energy collection technology is characterized by comprising:
the information acquisition module is used for acquiring real-time information of the marine sensing equipment in the current time slot in each time slot, wherein the real-time information comprises a task request indication value of a calculation task requested by the marine sensing equipment, channel power of the marine sensing equipment for accessing the fog node and battery energy level of the marine sensing equipment;
the model construction module is used for constructing calculation delay and energy consumption of a calculation task executed by the marine sensing equipment in the current time slot, transmission delay and energy consumption of unloading the calculation task to the fog node, constructing a calculation task execution cost model, a marine sensing equipment energy consumption model and an energy collection model, taking the minimum long-term average execution cost as an objective function, and constructing a calculation unloading model by combining the constructed model;
the unloading decision solving module is used for solving a calculation unloading model by utilizing a dynamic algorithm according to the acquired real-time information, and solving and obtaining an optimal calculation unloading decision of the current time slot;
the computational offload model is:
s.t.
wherein ,
，
computing task execution cost representing the t-th slot, < >>
Indicating penalty not performed after acquisition of the computational task,/->
A calculation delay representing the local execution of a calculation task by the marine sensor device in the t-th time slot, a->
Indicating that the marine sensing device is offloading computing tasks to fog in the t-th time slotTransmission delay of node->
Index representing computational offload model, +.>
=1 and
=1 means that at time slot t, the requested computation task is performed on the marine sensing device and the computation task is offloaded to the fog node;
solving an optimal computational offload decision in a current time slot using a dynamic algorithm, comprising:
constructing a Lyapunov function;
constructing a Lyapunov drift function and a Lyapunov drift plus penalty function according to the Lyapunov function;
simplifying and calculating an unloading model according to the Lyapunov drift function and the Lyapunov drift plus penalty function;
solving the simplified calculation unloading model to obtain an optimal calculation unloading decision in the current time slot;
the solving includes:
when the t time slot starts, acquiring a task request instruction value of a calculation task requested by the ocean sensing equipment in the current time slot
Battery energy level of marine sensor device +.>
And channel power of marine sensing device access fog node +.>
；
Substituting the information of the current time slot into the simplified calculation unloading model to solve, and determining the index of the calculation unloading model of the current time slot
Predetermined frequency of CPU cycle->
Transmitting power of ocean sensing equipment for accessing fog node
Partial energy reaching the marine sensor system based on energy harvesting technology>
；
And updating a battery energy queue of the ocean sensing equipment according to the result of calculation determination, judging whether a stable value of the current time slot is reached, namely judging whether constraint conditions of a calculation unloading model are met, if so, outputting the result of calculation determination as an optimal calculation unloading decision in the current time slot, otherwise, calculating an optimal calculation unloading decision of the next time slot.
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