We’ve talked about the evolution of science, and technology integration is one of them, and it’s a natural process to combine sensors and actuators. Wireless Sensor and Actor Network.

When the control node of the wireless sensor network needs to interact with the external physical world through the actuator, it needs to send instructions to the actuator, and the actuator converts the instructions into a physical behavior acting on the environment. Typical actuators can be people, control devices, or intelligent robots. With the increasingly mature and application of intelligent robot technology, the speed of research and development of small, intelligent, autonomous, low energy consumption and low cost actuator is accelerated, making it possible to build wireless sensor and actuator network.

WSAN consists of two parts: sensor node and actuator node. Their differences are mainly as follows:

• The sensor node is static and does not move, and the actuator node is mobile. The actuator node can be moved, which is beneficial to expand the effective area of the actuator node. A typical actuator can be a mobile robot. Multiple mobile robots can swim in the area covered by the sensor node and determine how the robots cooperate to complete the control function according to the data sent by the sensor. Of course, we can also use an intelligent robot as both the sensor node and the actuator node.
• The number of sensor nodes deployed in the monitoring area is huge, while the number of actuator nodes is not needed. The key is the execution capability. Sensor nodes are low cost, low power devices, its ability of sensing, computing, wireless communication and energy is limited, and actuator nodes can according to need, choose different types of actuators to implement different control function, relative to the sensor nodes, the actuator node with strong ability of data processing, high transmission power and longer battery life. According to the information transmitted by multiple sensor nodes, the actuator node decides how to cooperate to complete the control function.
• In traditional wireless sensor networks, sensor nodes transmit the perceived data to control nodes via sink nodes through multi-hop AD hoc networks, while WSAN requires sensor nodes
• It can cooperate with sensor node, sensor node and actuator node, and actuator node and actuator node.

(1) Heterogeneity WSAN is composed of heterogeneous components, including low-end sensor nodes and actuator nodes with strong processing capacity. These two parts have great differences in network communication, computing and storage capabilities. (2) Real-time WSAN is basically a closed-loop system, which conducts data processing, analysis and decision-making according to the data transmitted by sensors. In many applications, the actuator node is required to provide timely response to the perceptual information from sensors, so the network protocol should provide the guarantee of real-time data transmission. (3) collaborative sensor nodes and actuator nodes, actuator node and actuator node must maintain a good relationship between, because there can be multiple actuator nodes on the same event, the sensor node and actuator node collaboration can make the sensor event reporting data are transmitted to the most appropriate actuator node. After receiving the event report data, multiple actuator nodes cooperate with each other to take appropriate actions to complete the control task. (4) Mobility In WSAN, the actuator node needs to move to the corresponding position according to the event and perform the corresponding action.

• (1) Autonomous mechanism, that is, without the participation of the central controller, sensor nodes send their observed data to appropriate actuator nodes, which cooperate with each other to automatically complete the assignment of execution tasks and take appropriate cooperative actions. Advantages: Short latency, because the actuator is usually in or near the sensor/actuator area.
• (2) Semi-autonomous mechanism, similar to WSN structure, is applicable to most existing communication technologies. However, under the semi-autonomous mechanism, sensor data needs to be collected, analyzed and processed by the sink node, and then the execution instruction is sent to the execution node, so the delay from event occurrence to event processing is large. At the same time, the sink node as the central control node is prone to single point of failure, and the system reliability is poor.
• (3) Synergy mechanism
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In the cooperative mechanism, the sensor node first transmits the perception data to the actuator node through multi-hop or single-hop. After analyzing the data, the executor node negotiates with the sink node before taking actions (in three cases) : (1) informs the sink node of the actions to be taken by the executor node (only informs) (2) negotiates with the sink node about the actions to be taken; (3) Request action plan to control node through sink node. Wait for instructions.

1. _________________________________________________________________________ % % % %_________________________________________________________________________% clear all clc rng('default'); %% sets WNS override parameter, %% default input parameter is integer, if you want to define decimal, please multiply by the coefficient to integer and then convert. For example, range 1*1, R=0.03 can be converted to 100*100, R=3; % AreaX*AreaY*AreaZ AreaX = 100; AreaY = 100; AreaZ = 100; N = 20 ; % number of covered nodes R = 15; % Communication radius %% Set sparrow optimization parameter POP =30; % Population count Max_iteration=30; % set maximum number of iterations lb = ones(1,3*N); ub = [AreaX.*ones(1,N),AreaY.*ones(1,N),AreaZ.*ones(1,N)]; dim = 3*N; % dimension 3N,N coordinates fobj = @(X)fun(X,N,R,AreaX,AreaY,AreaZ); % fitness function [Best_pos,Best_score,SSA_curve]=SSA(POP,Max_iteration, LB, UB,dim,fobj); % start to optimize figure SSA_curve = 1-ssa_curve; % converts uncovered to uncovered. Plot (SSA_curve,'Color','r') title('Objective space') xlabel(' number of iterations '); Ylabel (' coverage '); Axis Tight grid on box on legend('SSA') display([' final optimal coverage: ', num2str(1-best_score)]); %Coordinate represents the storage of Coordinate points covered %CoordinateNumber represents the number of Coordinate points covered [Score,Coordinate,Best_pos,CoordinateNumber] = fun(Best_pos,N,R,AreaX,AreaY,AreaZ); figure plot3(Best_pos(1:N),Best_pos(N+1:2*N),Best_pos(2*N+1:3*N),'r.','MarkerSize',40); % Draw node hold on; plot3(Coordinate(1:CoordinateNumber,1),Coordinate(1:CoordinateNumber,2),Coordinate(1:CoordinateNumber,3),'b.','MarkerSiz E ', 0.1); % Draw overlay area hold off; Title (' ssa-wsN final result ') grid on; Legend ('WSN node ',' overlay ')Copy the code