Security Routing Method Based on Fuzzy Logic-Fish School Search Algorithm

Abstract

The present invention discloses a security routing method based on a fuzzy logic - fish swarm search algorithm, which includes: the source node determines whether there are food nodes in the water area, and if so, constructs a set of food nodes; uses the fuzzy logic method to calculate the food concentration corresponding to each food node in the set of food nodes in combination with the metric index; uses the fish swarm algorithm to find the food node with the highest food concentration in the set of food nodes as the optimal candidate relay node; sends a request control message to the optimal candidate relay node, and at the same time sets a delay timer; after receiving the request control message, the optimal candidate relay node determines whether there are food nodes in the water area, and if so, sends a feedback message to the source node; after receiving the feedback message within the time specified by the delay timer, the source node sends the data packet generated by it to the optimal candidate relay node; repeats the above process to achieve data transmission. The present invention can ensure the effective delivery of data packets and can suppress the routing hole problem.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and particularly relates to a security routing method based on a fuzzy logic - fish swarm search algorithm. Background Art

[0002] In recent years, countries around the world have been accelerating the pace of ocean development. Maritime activities such as environmental monitoring, ocean data collection, offshore oilfield exploration, port security, and tactical surveillance have also been continuously expanding. The research demand for underwater wireless communication networks in various countries has become increasingly urgent.

[0003] Underwater wireless communication networks are deployed in an extremely complex and variable water environment, with many differences from terrestrial wireless communication networks. Therefore, the existing routing protocols proposed for terrestrial wireless communication networks cannot be fully applied to the underwater wireless communication network environment. In addition, the emerging underwater applications have an increasing demand for high - speed and large - capacity underwater data transmission services. Obviously, some traditional terrestrial communication methods are restricted in underwater scenarios and cannot meet the needs of new services. For example, the water medium will cause severe attenuation of high - frequency radio frequency signals, which has a great impact on data transmission and is difficult to meet the high - speed communication requirements; while sound waves have disadvantages such as low capacity, small transmission bandwidth, and severe multipath effects. Compared with underwater electromagnetic wave communication and underwater acoustic communication, underwater wireless optical communication technology has advantages such as high bandwidth, large capacity, and strong anti - interference ability, and has gradually occupied a place in the research field. However, underwater wireless optical communication systems are mainly restricted by their directivity and short communication range. Nevertheless, the directivity and relatively limited communication range have played a positive role in promoting the unique security attributes of underwater wireless optical communication systems, because only intruders within the communication range of optical transceivers have the opportunity to eavesdrop / intercept messages. As a novel and promising research direction, the network scenario of using light waves for data communication has gradually received more and more attention from domestic and foreign researchers. Underwater wireless sensor networks, as a typical representative of underwater communication networks, have broad application prospects and demands in many fields.

[0004] The research on underwater wireless sensor network routing protocols can be roughly divided into centralized and distributed solutions. In the article "Modeling and performance analysis of multihop underwater optical wireless sensor networks (A. Celik, N. Saeed, T. Y. Al-Naffouri, M.-S. Alouini, Modeling and performance analysis of multihop underwater optical wireless sensor networks, in: IEEE Wireless Commun. and Netw. Conf., (WCNC), 2018, pp. 1–6.)", a centralized routing solution considering the underwater propagation characteristics of light beams was proposed. This routing protocol assumes that the system has an accurate PAT mechanism and node location information, and solves the shortest path problem through Dijkstra's algorithm. In the research work on the entire underwater wireless sensor network routing protocol, the distributed routing protocol accounts for a relatively large proportion. In the article "VBF: vector-based forwarding protocol for underwater sensor networks (Xie, Peng, Jun-Hong Cui, Li Lao. VBF: vector-based forwarding protocol for underwater sensor networks [C]. / / Berlin: Springer, 2006.)", the vector-based forwarding routing protocol VBF was proposed. Implementing the VBF routing protocol requires each node to be able to sense its own location. It adopts the idea similar to the virtual pipeline vector to form a virtual pipeline from the source node to the destination node. All data packets are forwarded to the destination node through the pipeline, and only the nodes within the range close to the pipeline can participate in forwarding data. To avoid the complexity of obtaining underwater node location information, the depth-based routing protocol simplifies the node location information. In the article "DBR: Depth-Based Routing for Underwater Sensor Networks proposed the depth-based opportunistic routing protocol DBR (Hai Y, Shi Z J, Cui JH. DBR: Depth-Based Routing for Underwater Sensor Networks [C]. / / In the "VBF: Vector-Based Forwarding for Underwater Wireless Sensor Networks (In: International Ifip-tc6 Networking Conference on Adhoc&Sensor Networks: Springer-Verlag, 2008.)", a node only needs to know its own depth information and forwards data packets to the water surface in a greedy forwarding manner. If the depth of the node receiving the data packet is less than the depth embedded in the data packet, the node will forward the data packet; otherwise, it will discard the data packet. Rawan et al. proposed a distributed routing protocol for underwater optical wireless sensor networks, namely the sector-based routing protocol SRP (R. Alghamdi, N. Saeed, H. Dahrouj, T. Y. Al-Naffouri, M. Alouini, On distributed routing in underwater optical wireless sensor networks, CoRR abs / 1811.05308 (2018)). In SRP, the network is divided into four quadrants according to the positions of the forwarding node and the destination node. One quadrant with the same direction as the position of the destination node is selected, and the bit error rates of each node in this quadrant are compared. The source node selects the node with the minimum bit error rate in the quadrant as the forwarding node and iterates this process until the destination node is found.)

[0005] However, although the centralized routing protocol provides better end-to-end performance, it requires prior knowledge of the global network topology, which leads to high communication overhead and energy consumption in the whole network. The distributed routing protocol does not need to know the whole network information. Instead, when there is a communication need, it dynamically makes routing decisions based on the information of the source node, forwarding node, and destination node. Therefore, it is more suitable for dynamic network environments. Moreover, the routing metric information considered in most existing routing protocols is not sufficient. For example, VBF, DBR, SRP, etc. only consider the position information of nodes when making routing decisions, while problems such as signal loss in water and limited node energy are ignored. And it is often difficult to accurately describe the situation considering multiple metrics through specific mathematical formulas. At the same time, most routing schemes in the design do not consider the problem of avoiding routing holes.) Summary of the Invention

[0006] To solve the above problems existing in the prior art, the present invention provides a security routing method based on fuzzy logic - fish swarm search algorithm. The technical problems to be solved by the present invention are realized through the following technical solutions:

[0007] An embodiment of the present invention provides a security routing method based on a fuzzy logic - fish swarm search algorithm, which is applied to a distributed underwater wireless sensor network. The network includes a surface sink node and M sensor nodes distributed underwater, where M is an integer greater than 0, and the method includes:

[0008] The source node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If there are, it constructs a set of food nodes. The source node is one of the M sensor nodes distributed underwater, and the source node is the node that generates data packets.

[0009] The source node uses the fuzzy logic method to calculate the food concentration corresponding to each food node in the food node set in combination with the metric index.

[0010] The source node uses the fish swarm algorithm to find the food node with the highest food concentration in the food node set as the optimal candidate relay node.

[0011] The source node sends request control information to the optimal candidate relay node and sets a delay timer at the same time.

[0012] After receiving the request control information, the optimal candidate relay node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If there are, it sends feedback information to the source node.

[0013] After receiving the feedback information within the time specified by the delay timer, the source node sends the data packet it generates to the optimal candidate relay node.

[0014] Take the optimal candidate relay node as the relay node. This relay node performs the same process as the source node to find the optimal candidate relay node until finally a surface sink node is found within the communication range, realizing the data transmission from the source node to the surface sink node.

[0015] In an embodiment of the present invention, the source node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If not, the source node discards the data packet it generates.

[0016] In an embodiment of the present invention, the optimal candidate relay node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If not, the optimal candidate relay node does nothing.

[0017] In an embodiment of the present invention, if the source node does not receive the feedback information within the time specified by the delay timer, it deletes the optimal candidate relay node from the food node set.

[0018] The source node re - uses the fish - swarm algorithm to find the food node with the highest food concentration from the remaining food nodes in the food node set as the new optimal candidate relay node;

[0019] The source node sends request control information to the optimal candidate relay node and sets a delay timer simultaneously;

[0020] The new optimal candidate relay node determines whether there are food nodes in the water area through the beacon - assisted information it sends. If there are, it sends feedback information to the source node;

[0021] After the source node receives the feedback information within the time specified by the delay timer, it sends the data packet it generates to the optimal candidate relay node;

[0022] Take the new optimal candidate relay node as the relay node. This relay node performs the same process as the source node to find the optimal relay node until finally a water - surface convergence node is found within the communication range, realizing the data transmission from the source node to the water - surface convergence node.

[0023] In an embodiment of the present invention, the source node uses the fuzzy - logic method to calculate the food concentration corresponding to each food node in the food node set in combination with metric indicators, including:

[0024] Select the packet delivery ratio, forward - diffusion backward - deviation, relative motion speed, distance progress, and node remaining energy as metric indicators;

[0025] Select the membership functions of the metric indicators. The source node performs fuzzification - defuzzification according to the membership functions and fuzzy - rule definitions of the metric indicators to calculate the food concentration corresponding to each food node in the food node set.

[0026] In an embodiment of the present invention, a membership function combining a triangle and a trapezoid is selected as the membership function of the metric indicators; correspondingly,

[0027] The packet delivery ratio includes two membership types: low and high; among them, low means the delivery rate is lower than 30%; high means the delivery rate is higher than 70%;

[0028] The forward - diffusion backward - deviation includes three membership types: close, moderate, and deviated; among them, close means the sum of the forward - diffusion transmission angle and the backward - deviation angle ≤ π / 2; deviated means the sum of the forward - diffusion transmission angle and the backward - deviation angle ≥ 3π / 2; moderate means the sum of the forward - diffusion transmission angle and the backward - deviation angle = π;

[0029] The relative motion speed includes three membership types: slow, moderate, and fast; among them, slow means the relative motion speed of the node is lower than 0.2v max vmax represents the maximum speed of the node; medium represents that the relative movement speed of the node is equal to 0.5v max ; fast represents that the relative movement speed of the node is higher than 0.8v max ;

[0030] The distance progress includes two membership types: large and small; among them, large means the distance progress is greater than 0.8R, where R represents the communication radius of the node; small means the distance progress is less than 0.2R;

[0031] The remaining energy of the node includes three membership types: much, medium, and little; among them, much means the remaining energy of the node ≥ 0.8E initial , E initial represents the initial energy of the node; medium means the remaining energy of the node = 0.5E initial ; little means the remaining energy of the node ≤ 0.2E initial .

[0032] In an embodiment of the present invention, the fuzzy rule is defined as fuzzy describing the corresponding link quality according to the membership type combinations corresponding to the packet delivery rate, forward diffusion-backward drift deviation, relative movement speed, distance progress, and remaining energy of the node respectively.

[0033] In an embodiment of the present invention, the source node performs fuzzification-defuzzification according to the membership function of the metric index and the defined fuzzy rule, and calculates the food concentration corresponding to each food node in the food node set, including:

[0034] Calculating the membership values of the five metric indexes corresponding to each food node in the food node set according to the membership function of the metric index;

[0035] According to the defined fuzzy rule, and using the IF / THEN rule for fuzzy logic reasoning;

[0036] Defining the membership function of the link quality, and using the area centroid method to implement the defuzzification operation, and calculating the link quality value between the source node and each food node;

[0037] Evaluating the food concentration corresponding to each food node in the food node set according to the link quality value.

[0038] In an embodiment of the present invention, each sensor node is equipped with an acoustic-optical communication device;

[0039] Sending beacon auxiliary information through the acoustic device;

[0040] Implementing the transmission of data packets, request control information, and feedback information between sensor nodes and between sensor nodes and the water surface aggregation node through the optical communication device.

[0041] In one embodiment of the present invention, the food node is a sensor node that has a positive forward progress according to the distance progress metric and whose remaining energy exceeds the energy threshold.

[0042] Advantages of the present invention:

[0043] The security routing method based on the fuzzy logic - fish swarm search algorithm proposed by the present invention selects an underwater acoustic - optical hybrid sensor network mainly based on light waves and supplemented by sound waves as the research background. Based on the characteristics of the underwater wireless optical communication channel and starting from the problems existing in the underwater sensor network and optical communication, a security routing method based on the fuzzy logic - fish swarm search algorithm is proposed. Specifically: The source node determines whether there are food nodes in the water area through the beacon - assisted information sent by itself. If there are, a food node set is constructed; the source node uses the fuzzy logic method to calculate the food concentration corresponding to each food node in the food node set in combination with the metric; the source node uses the fish swarm algorithm to find the food node with the highest food concentration in the food node set as the optimal candidate relay node; the source node sends a request control message to the optimal candidate relay node and sets a delay timer at the same time; after receiving the request control message, the optimal candidate relay node determines whether there are food nodes in the water area through the beacon - assisted information sent by itself. If there are, it sends a feedback message to the source node; after receiving the feedback message within the time specified by the delay timer, the source node sends the data packet generated by it to the optimal candidate relay node; taking the optimal candidate relay node as the relay node, this relay node executes the same process as the source node to find the optimal candidate relay node until finally a surface convergence node is found within the communication range, realizing the data transmission from the source node to the surface convergence node. This method improves the greedy forwarding routing strategy by using an optimization algorithm for the routing message situation of sensor nodes in the underwater wireless sensor network. It has high survivability. The proposed routing method can resist the adverse effects of the harsh water environment on the network performance and overcome the link instability problem caused by node mobility. It has high practical value for network scenarios with time - varying topologies, can ensure the effective delivery of data packets while improving the energy utilization rate of nodes, and to a certain extent suppress the routing hole problem caused by the sparse distribution of nodes in some areas of the network.

[0044] The present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0045] Figure 1 It is a schematic structural diagram of a distributed underwater wireless sensor network provided by an embodiment of the present invention;

[0046] Figure 2 It is a schematic flow diagram of a security routing method based on the fuzzy logic - fish swarm search algorithm provided by an embodiment of the present invention;

[0047] Figure 3 It is a schematic diagram of the corresponding angle in the forward diffusion-backward dissociation deviation provided by the embodiment of the present invention;

[0048] Figure 4 (a) to 4(e) are schematic diagrams of the membership functions corresponding to five metric indicators provided by the embodiment of the present invention;

[0049] Figure 5 It is a schematic diagram of the link quality membership function corresponding to the realization of link fuzzy estimation according to the definition of fuzzy rules provided by the embodiment of the present invention;

[0050] Figure 6 It is a schematic diagram of the structure of an electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0051] The following further describes the present invention in detail with reference to specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0052] The complexity of the underwater environment brings difficulties to the evaluation of the performance of the underwater wireless optical communication system, and the mobility of underwater nodes will cause problems such as real-time changes in the neighbor nodes of the nodes and unstable link states, which will cause frequent packet loss and retransmission during routing forwarding, thus further exacerbating the deterioration of the network service quality. In addition, problems such as routing holes caused by the failure of some nodes and the exhaustion of node energy due to long-term operation will further deteriorate the performance of the network, such as an increase in the end-to-end transmission delay and a decrease in the packet delivery rate. Therefore, how to implement a routing algorithm for an underwater wireless sensor network with high survivability and meeting performance requirements is crucial.

[0053] In order to improve the overall performance of the underwater wireless sensor network, please refer to Figure 1, an embodiment of the present invention considers a distributed underwater wireless sensor network, which includes a sink node on the water surface and M sensor nodes distributed underwater, where M is an integer greater than 0. The sink node deployed on the water surface is responsible for collecting data from the underwater sensor nodes and transmitting this data to the onshore base station through radio signals. Each sensor node is equipped with acoustic-optical communication equipment, and packet data, request control information, and feedback information are transmitted between sensor nodes and between sensor nodes and the sink node on the water surface through the optical communication equipment. Among them, the acoustic equipment with the characteristics of omnidirectional coverage and long-distance communication can be used to send beacon auxiliary information to detect the surrounding environment to achieve food node detection. Since the monitoring information of the sensor is usually only valid for its sensing location, it is assumed that each sensor node can obtain its own and other neighbors' location information through the beacon auxiliary information sent by the acoustic equipment. The underwater location information can be obtained through a hybrid acoustic / optical network positioning method, and the hybrid acoustic-optical link improves the positioning accuracy, enables the optical transceiver to achieve precise alignment, and improves the end-to-end link quality. Here, the positioning method is not the focus of the research of the present invention and will not be introduced in detail here. The present invention mainly focuses on the routing process of transmitting the data packet generated by a certain underwater sensor node to the sink node on the water surface.

[0054] For the routing process of transmitting the data packet generated by a certain underwater sensor node to the sink node on the water surface, please refer to Figure 2 , an embodiment of the present invention provides a security routing method based on a fuzzy logic-fish swarm search algorithm, which is applied to Figure 1 the distributed underwater wireless sensor network shown in the figure. A security routing method based on a fuzzy logic-fish swarm search algorithm is proposed by using the foraging, following, aggregation, and newly added diffusion behaviors in the fish swarm search algorithm to improve the performance of the underwater sensor network. This routing method is an improvement on the greedy forwarding routing. Since the greedy forwarding routing strategy only detects and establishes communication links when there is a communication need and does not need to maintain the link in real time and exchange status information with neighbors constantly like traditional unicast routing, it saves a lot of overhead and is more suitable for networks with time-varying network topologies; the omnidirectional property of sound waves is used to detect neighbor nodes (food nodes), and at the same time, the safe property of laser is used to complete the message transmission between nodes. The fuzzy logic-fish swarm search algorithm is used to improve the greedy routing to achieve accurate measurement and optimal decision-making. Correspondingly, the embodiment of the present invention specifically includes the following steps:

[0055] The source node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If there are, a food node set is constructed; the source node is one of the M sensor nodes distributed underwater, and the source node is the node that generates the data packet;

[0056] The source node uses the fuzzy logic method to combine the metric indicators to calculate the food concentration corresponding to each food node in the food node set;

[0057] The source node uses the fish swarm algorithm to find the food node with the highest food concentration in the food node set as the optimal candidate relay node;

[0058] The source node sends request control information to the optimal candidate relay node and sets a delay timer at the same time;

[0059] After receiving the request control information, the optimal candidate relay node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If there are, it sends feedback information to the source node;

[0060] After receiving the feedback information within the time specified by the delay timer, the source node sends the data packet it generates to the optimal candidate relay node;

[0061] Take the optimal candidate relay node as the relay node, and this relay node executes the same process as the source node to find the optimal candidate relay node until finally a water surface convergence node is found within the communication range, realizing the data transmission from the source node to the water surface convergence node.

[0062] Furthermore, for the source node to determine whether there are food nodes in the water area through the beacon auxiliary information sent by itself, if not, the source node discards the data packet it generates.

[0063] Furthermore, for the optimal candidate relay node to determine whether there are food nodes in the water area through the beacon auxiliary information sent by itself, if not, the optimal candidate relay node does nothing.

[0064] Furthermore, if the source node does not receive the feedback information within the time specified by the delay timer, the optimal candidate relay node is deleted from the food node set;

[0065] The source node re-uses the fish swarm algorithm to find the food node with the highest food concentration from the remaining food nodes in the food node set as the new optimal candidate relay node;

[0066] The source node sends request control information to the optimal candidate relay node and sets a delay timer at the same time;

[0067] The new optimal candidate relay node determines whether there are food nodes in the water area through the beacon auxiliary information sent by itself. If there are, it sends feedback information to the source node;

[0068] After receiving the feedback information within the time specified by the delay timer, the source node sends the data packet it generates to the optimal candidate relay node;

[0069] The new optimal candidate relay node is used as the relay node, and this relay node performs the same process as the source node to find the optimal relay node until finally a water surface aggregation node is found within the communication range, realizing the data transmission from the source node to the water surface aggregation node.

[0070] Suppose there is a node with communication requirements in the network, denoted here as the source node, which initiates the foraging behavior first. At this time, the source node will generate a beacon-assisted message, namely the fish school message, to judge the food nodes in the water area. Here, in the embodiments of the present invention, the food node is defined as a sensor node in the network that has a positive forward progress and the remaining energy exceeds the energy threshold. When the distance progress metric of the current node is greater than 0, it is considered that the node has a positive forward progress. The specific distance progress metric will be introduced in detail later. This can reduce the number of hops of message transmission, thereby reducing the overall transmission delay. The beacon-assisted message is sent through the acoustic system to achieve omnidirectional coverage. In the foraging behavior, assume that the current state of a certain fish at the source node is X i , and this fish randomly searches for a target state of X j within its field of vision, that is, within the effective communication range of the node, which is expressed as:

[0071] X j = X i + VisualgRand();

[0072] where VisualgRand() represents randomly selecting other nodes within the fish's field of vision (i.e., all nodes within the communication range). Calculate their distance forward progress respectively, and judge whether the position belongs to a food node based on this. If it is a food node, the fish moves in the direction of the target state X j which is expressed as:

[0073]

[0074] where, represents the state of the fish at the current moment, represents the state of the fish at the next moment, X j represents the target state of the fish, and StepgRand() represents the random step length of the fish's movement. Here, it is stipulated that the fish school message can move between two nodes within one hop range, that is, the step length is 1.

[0075] The fish school searches for food nodes in the surrounding environment through the above foraging behavior. If a food node is found, the food node is first added to the food node set.

[0076] Further, in the embodiments of the present invention, the source node uses the fuzzy logic method to calculate the food concentration corresponding to each food node in the food node set in combination with the metric, including:

[0077] Select the packet delivery rate, forward diffusion-backward drift deviation, relative motion speed, distance progress, and remaining energy of nodes as metrics; select the membership functions of the metrics, and the source node performs fuzzification-defuzzification according to the membership functions of the metrics and the fuzzy rule definitions, and calculates the food concentration corresponding to each food node in the food node set.

[0078] Here, first, a detailed analysis and description of the five key metrics of the selected data delivery rate, forward diffusion-backward drift deviation, relative motion speed, distance progress, and remaining energy of nodes are carried out as follows:

[0079] (1). Packet delivery rate

[0080] Affected by various underwater environmental factors, the network quality often shows poor performance. A large bit error rate will directly cause serious errors in the transmitted data packets and be discarded. Therefore, underwater optical wireless communication (UOWC) usually uses the bit error rate as the key indicator to measure the reliability of network communication. The present invention first sets a suitable bit error rate threshold to ensure effective communication in the system link. Then, based on this bit error rate threshold, the maximum communication range of each underwater sensor node can be deduced. At the same time, considering the bit error rate as one of the routing metrics, the reliability of data transmission is ensured, and the packet delivery rate is improved.

[0081] The characteristics of the underwater optical wireless communication channel are analyzed and studied, considering three main underwater channel impairment factors, namely attenuation, turbulence, and pointing error.

[0082] Attenuation is mainly manifested as the path loss suffered by data during transmission on the communication link. On the transmission path with an extinction coefficient of c(λ) and a link distance of d, the path loss h of a laser with an average transmitted optical power of P(0) l Can be characterized by the Beer-Lambert law as:

[0083]

[0084] Among them, P(0) represents the average transmitted optical power of the laser in the link, d represents the transmission link distance, P(d) represents the received optical power after the transmission link distance d, c(λ) represents the extinction coefficient, η T And η R Respectively represent the photoelectric conversion efficiencies of the transmitter and the receiver, L represents the longitudinal distance of the link, θ0 represents the initial divergence angle of the laser beam, and A rec Represents the aperture area of the receiver.

[0085] Turbulence, select the salinity-induced ocean turbulence model with a Weibull distribution form, which has the following form:

[0086]

[0087] Among them, β1 represents the shape parameter related to the scintillation index, β2 represents the scale parameter related to the average value of ocean turbulence, and h a represents the ocean turbulence channel state. The scintillation index is defined by the parameters β1 and β2 as:

[0088]

[0089] For a Gaussian beam, the pointing error h at the waist position at a distance L from the receiving plane caused by the geometric diffusion of the optical wave p The probability density function of can be jointly determined by the detector aperture size, beam width, and beam jitter variance, and the pointing error can be expressed as:

[0090]

[0091] Among them, A0 represents the average collection power, A0 = [erf(v)] 2 , erf(g) represents the complementary error function, a represents the receiver aperture radius, ω L represents the beam width of the receiving plane, represents the equivalent beam width, and r represents the radial distance between the center of the receiver plane and the central axis of the laser beam.

[0092] From the above three channel impairment factors of attenuation, turbulence, and pointing error, a multiplicative composite channel fading model can be directly obtained, expressed as:

[0093] h = h l h a h p ;

[0094] Finally, the average bit error rate calculation formula is expressed as:

[0095]

[0096] Among them, P e (h) represents the conditional bit error rate, and f h (h) represents the probability density function of the channel state.

[0097] The packet error rate formula between any two sensor nodes is expressed as:

[0098]

[0099] Among them, G is the length of the packet.

[0100] Furthermore, the formula for the packet delivery ratio between any two sensor nodes is expressed as:

[0101]

[0102] (2), Forward diffusion - Backward deviation

[0103] In the present invention, the concept of forward diffusion - backward deviation is introduced. Among them, forward diffusion is represented by the transmission angle θ T , which is described as the included angle between the source node - the next - hop sensor node and the source node - the destination node (the water - surface aggregation node); at the same time, the backward deviation Δθ i is defined as the included - angle size formed by the moving direction of the next - hop sensor node and the direction of the next - hop sensor node - the destination node. Through analysis, it can be obtained that a smaller forward - transmission angle θ T and the backward deviation Δθ i can, to a certain extent, limit the transmission direction of the message and prevent the outward diffusion from deviating from the destination. By introducing the consideration of this factor, the routing - selection strategy of the algorithm can tend to find a route with a trend of approaching the destination node. In this way, the spatial distance of the route can be shortened, and the number of routing - forwarding hops can also be correspondingly reduced, thereby reducing the transmission delay.

[0104] Correspondingly, the formula for calculating the normalized transmission angle is expressed as:

[0105]

[0106] where d sd , d si , d id respectively represent the distances between the source node - the destination node, the source node - a certain neighbor sensor node i, and a certain neighbor sensor node i - the destination node.

[0107] Assume that the coordinates of the destination water - surface aggregation node are (x d , y d ), the coordinates and its moving direction of a certain neighbor sensor node i are (x i , y i ) and θ i , then the normalized direction deviation can be expressed by the formula:

[0108]

[0109] Then the metric formula for forward diffusion - backward deviation is expressed as:

[0110]

[0111] (3), Relative motion speed

[0112] Since the present invention considers using a laser for actual data communication scenarios, the stability of the link is easily affected by the movement of underwater sensor nodes. If the moving speeds between nodes are closer, the link stability between nodes is higher. Therefore, finding sensor nodes with the same or similar moving speeds and moving directions can improve the link stability. The present invention uses the relative motion speed as one of the metric indicators to measure the stability of the link between two nodes. Assume that at a certain moment, the moving speeds and directions of any two nodes i and j are v i and θ i , v j and θ j , respectively. Then the relative speed between the two can be expressed by the formula after normalization as:

[0113]

[0114] where v max represents the maximum speed of the moving node, and the value of Δv ij is between [0, 1]. The closer the value is to 0, the smaller the relative speed between node i and node j, and the better the link stability between the two. Conversely, the link stability is worse.

[0115] (4), Distance progress

[0116] Distance progress is one of the commonly used metric indicators in opportunistic routing, and it is a metric strategy for selecting appropriate relay nodes according to the degree of proximity of nodes to the destination node. The greater the distance progress, the closer the relay node is to the destination node, so unnecessary hops can be avoided, thereby shortening the time to reach the destination node. The definition formula of the normalized distance progress in the present invention is expressed as:

[0117]

[0118] where d id , d jd are the distances between node i and node d, and between node j and node d respectively, and R represents the communication radius of the node.

[0119] (5), Node remaining energy

[0120] Underwater sensor network nodes are generally powered by batteries carried by the nodes themselves. Since they are deployed in the water environment, it is difficult and costly to replace the batteries or recharge them. Therefore, the energy of each node is often limited. If the usage frequency of some nodes is high, resulting in their energy being exhausted, problems such as link interruption and routing holes may occur, further causing an increase in the average end-to-end delay, a decrease in the packet delivery rate, and a reduction in throughput in the network. Therefore, improving energy efficiency and maintaining a long network lifetime are one of the key issues to be considered in the design of underwater wireless sensor network routing algorithms. First, a suitable energy threshold is selected. Nodes with energy lower than this threshold are considered to have too low energy and no longer participate in the packet transmission process. Similarly, the present invention selects the ratio of the remaining energy of the node as one of the routing metric indicators to improve the energy utilization rate of the node and balance the network load. The formula for the ratio of the remaining energy of the node in the present invention can be expressed as:

[0121]

[0122] where E c represents the ratio of the remaining energy of the node, E current (t) represents the remaining energy of the node, and E initial represents the initial energy of the node.

[0123] Next, fuzzy logic is used to make decisions on five routing attributes of the nodes, and the optimal candidate relay node is selected according to the selected routing criteria. In the embodiment of the present invention, the fuzzy logic method is used to evaluate each metric indicator and select the next-hop node based on this. Since it is difficult to describe the dynamic changes of the network using specific mathematical formulas, and fuzzy logic, as a method similar to human reasoning, uses non-numerical linguistic variables for description and can more accurately and dynamically adaptively optimize parameters, the embodiment of the present invention selects to process inaccurate information through the fuzzy logic method to improve decision-making and performance.

[0124] Based on the characteristics of underwater wireless optical communication and according to the characteristics of wireless ad hoc networks in underwater application scenarios, a membership function combining a triangle and a trapezoid is selected as the membership function of the metric indicators; the link estimation of five metric indicators is defined using fuzzy sets, please refer to Figure 4 (a)~ Figure 4 (e), correspondingly,

[0125] The packet delivery rate includes two membership types: low and high; among them, low means that the delivery rate is lower than 30%; high means that the delivery rate is higher than 70%; by Figure 4(a) As shown, when the delivery rate is lower than 30%, it completely belongs to the low membership type at this time. When the delivery rate is higher than 70%, it completely belongs to the high membership type at this time. And when it is between 30% and 70%, it is incompletely subordinate at this time. For example, the point circled by the ellipse in the figure has a membership degree of 0.8 for low and a membership degree of 0.2 for moderate; the understanding of the following 4(b)~ Figure 4 (e) is similar to Figure 4 (a), and no further examples will be given;

[0126] The forward diffusion-backward dissociation deviation includes three membership types: close, moderate, and deviated. Among them, close means that the sum of the forward diffusion transmission angle and the backward dissociation difference angle ≤ π / 2; deviated means that the sum of the forward diffusion transmission angle and the backward dissociation difference angle ≥ 3π / 2; moderate means that the sum of the forward diffusion transmission angle and the backward dissociation difference angle = π;

[0127] The relative motion speed includes three membership types: slow, moderate, and fast. Among them, slow means that the relative motion speed of the node is lower than 0.2v max , v max represents the maximum speed of the node; moderate means that the relative motion speed of the node is equal to 0.5v max ; fast means that the relative motion speed of the node is higher than 0.8v max ;

[0128] The distance progress includes two membership types: large and small. Among them, large means that the distance progress is greater than 0.8R, where R represents the communication radius of the node; small means that the distance progress is less than 0.2R;

[0129] The remaining energy of the node includes three membership types: much, moderate, and little. Among them, much means that the remaining energy of the node ≥ 0.8E initial , E initial represents the initial energy of the node; moderate means that the remaining energy of the node = 0.5E initial ; little means that the remaining energy of the node ≤ 0.2E initial .

[0130] The fuzzy rules of the embodiment of the present invention are defined as fuzzy descriptions of the corresponding link quality according to the membership type combinations corresponding to the data packet delivery rate, forward diffusion-backward dissociation deviation, relative motion speed, distance progress, and remaining energy of the node. Specifically, for multiple link parameters, fuzzy definitions are made. Based on the five metric parameters of the link estimation as inputs and combined with the fuzzy rules defined in Table 1, a fuzzy description of the link quality level can be mapped. Please refer to Figure 5 , which can be represented by {excellent; very good; good; good above average; good middle 1; good middle 2; good below average; moderate above average; moderate middle 1; moderate middle 2; moderate below average; poor above average; poor middle; poor below average; very poor; extremely poor; terrible} respectively.

[0131] Table 1 Fuzzy Rule Definition Table

[0132]

[0133] Generally, fuzzy logic generally includes three steps: fuzzifying the input, fuzzy processing, and defuzzification. The input step converts numerical values into fuzzy languages, processes them using fuzzy rules in the form of IF / THEN, and finally outputs in numerical form through defuzzification. Then, in the embodiments of the present invention, fuzzification - defuzzification is performed according to the membership function and fuzzy rule definition of the metric index, and the food concentration corresponding to each food node in the food node set is calculated, including:

[0134] Calculating the membership values of five metric indexes corresponding to each food node in the food node set according to the membership function of the metric index; performing fuzzy logic reasoning according to the fuzzy rule definition and using the IF / THEN rule; defining the membership function of the link quality, and implementing the defuzzification operation using the area centroid method to calculate the link quality value between the source node and each food node; evaluating the food concentration corresponding to each food node in the food node set according to the link quality value. Here, according to Figure 4 (a)~ Figure 4 (e), and the fuzzy rule definition given in Table 1, calculating the membership values of five metric indexes corresponding to each food node in the food node set, performing fuzzy logic reasoning using the IF / THEN rule, defining the membership function of the link quality as Figure 5 shown, implementing the defuzzification operation using the area centroid method, and calculating the link quality value between the source node and each food node. The formula is expressed as:

[0135]

[0136] where z is the fuzzy variable, μ(z) is the membership function of the link quality, and the defined membership function of the link quality can be defined according to existing methods. After calculating the link quality, in the embodiments of the present invention, the link quality value between the source node and each food node is selected as the food concentration corresponding to the food node.

[0137] It can be seen that the higher the data transmission success rate in the embodiments of the present invention, the smaller the deviation from the straight line between the source node and the destination node, the closer the movement direction and speed are, and the higher the food concentration of the node closer to the destination node. When the food concentration at the food node discovered by one or several fish is relatively large, other companions will follow and gather at the food node. Suppose the current state of a certain fish's location is X i , its food concentration is F(X i ), and it searches for the food concentration F(X j ) of the nearby partner's location X j), if the food concentration F(X j ) > F(X i ) and the food node is valid (i.e., the remaining energy of the node is greater than the energy threshold), then the fish executes a following behavior, and at the same time sets the partner position X j as the area with the highest food concentration. This process is executed cyclically, and the number of executions depends on the number of partners in the neighborhood. After the cycle ends, the food node with the highest food concentration can be found.

[0138] If the food node at the partner position is valid but the food concentration is lower than the food concentration value at the current position, the fish school stays still. Eventually, the fish school will gather at the food node with the highest food concentration, and this position is the optimal solution to be found for routing and forwarding. If the food node fails, that is, the energy is lower than the threshold level, the fish school originally attached to that place executes a diffusion behavior, and at the same time deletes the failed node from the set of food nodes. If the failed node is the node with the highest food concentration, it is deleted from the area with the highest food concentration, and an attempt is made to find a sub-optimal solution, that is, the optimal candidate relay node; otherwise, the diffused fish school directly follows and gathers in the area with the highest food concentration. If after multiple attempts to find, the fish school does not move at all, it means that there is no suitable optimal candidate relay node within the neighborhood range of the current source node, then the source node discards the data packet.

[0139] Different from the fact that in the previous greedy forwarding routing, the high-priority node that receives the data packet directly has the forwarding qualification, in the routing of the embodiment of the present invention, after the optimal candidate relay with the highest food concentration is found through the foraging behavior, the optimal candidate relay node further conducts a foraging survey on the neighbors within its field of vision. In the routing algorithm, it is determined whether the node carries a data packet to distinguish whether the source node or the optimal candidate relay node executes the foraging behavior. Here, the source node uses an accurate tracking system to track the optimal candidate relay node, so as to ensure that the subsequent laser communication process can proceed normally. The optimal candidate relay node first detects whether there are valid food nodes around it. It has the forwarding qualification only when there are valid food nodes around the node. Otherwise, the fish school executes a diffusion behavior and re-searches for the optimal candidate relay node. By improving this key step, the nodes among the original high-priority nodes that do not have better forwarding conditions are directly eliminated, and the lower-priority nodes with abundant food nodes instead have the possibility of forwarding data packets, which can effectively avoid the routing hole problem that may be encountered in the networking process, and can also play a positive role in balancing the network load to a certain extent.

[0140] In summary, the security routing method based on the fuzzy logic - fish swarm search algorithm proposed in the embodiments of the present invention selects the underwater acoustic - optical hybrid sensor network mainly based on light waves and supplemented by sound waves as the research background. Based on the characteristics of the underwater wireless optical communication channel and starting from the problems existing in the underwater sensor network and optical communication, a security routing method based on the fuzzy logic - fish swarm search algorithm is proposed. Specifically: The source node determines whether there are food nodes in the water area through the beacon - assisted information it sends. If there are, it constructs a set of food nodes. The source node uses the fuzzy logic method combined with the metric index to calculate the food concentration corresponding to each food node in the set of food nodes. The source node uses the fish swarm algorithm to find the food node with the highest food concentration in the set of food nodes as the optimal candidate relay node. The source node sends a request control message to the optimal candidate relay node and sets a delay timer at the same time. After receiving the request control message, the optimal candidate relay node determines whether there are food nodes in the water area through the beacon - assisted information it sends. If there are, it sends a feedback message to the source node. After receiving the feedback message within the time specified by the delay timer, the source node sends the data packet it generates to the optimal candidate relay node. Taking the optimal candidate relay node as the relay node, this relay node performs the same process as the source node to find the optimal candidate relay node until finally a surface aggregation node is found within the communication range, realizing the data transmission from the source node to the surface aggregation node. This method improves the greedy forwarding routing strategy by using an optimization algorithm for the routing message situation of sensor nodes in the underwater wireless sensor network, and has high survivability. The proposed routing method can resist the adverse effects of the harsh water environment on the network performance and overcome the link instability problem caused by node mobility. It has high practical value for network scenarios with time - varying topologies, can ensure the effective delivery of data packets while improving the energy utilization rate of nodes, and to a certain extent inhibits the routing hole problem caused by the sparse distribution of nodes in some areas of the network.

[0141] Please refer to Figure 6 , the embodiments of the present invention provide an electronic device, including a processor 601, a communication interface 602, a memory 603, and a communication bus 604. Among them, the processor 601, the communication interface 602, and the memory 603 complete mutual communication through the communication bus 604;

[0142] The memory 603 is used to store computer programs;

[0143] When the processor 601 is used to execute the program stored on the memory 603, it realizes the steps of the above - mentioned security routing method based on the fuzzy logic - fish swarm search algorithm.

[0144] An embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned security routing method based on the fuzzy logic-fish swarm search algorithm are implemented.

[0145] For the electronic device / storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For related parts, please refer to the partial description of the method embodiment.

[0146] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0147] Although the present invention has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by referring to the specification and its drawings. In the specification, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality of cases. Certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0148] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A security routing method based on fuzzy logic - fish school search algorithm, characterized in that, Applied to a distributed underwater wireless sensor network, which includes a surface sink node and M sensor nodes distributed underwater, where M is an integer greater than 0, and it includes: The source node determines whether there are food nodes in the water area based on the beacon auxiliary information sent by itself. If there are, it constructs a food node set; the source node is one of the M sensor nodes distributed underwater, and the source node is the node that generates data packets. The source node uses the fuzzy logic method combined with metric indicators to calculate the food concentration corresponding to each food node in the food node set. The source node uses the fish swarm algorithm to find the food node with the highest food concentration in the food node set as the optimal candidate relay node. The source node sends request control information to the optimal candidate relay node and sets a delay timer at the same time. After receiving the request control information, the optimal candidate relay node determines whether there are food nodes in the water area based on the beacon auxiliary information sent by itself. If there are, it sends feedback information to the source node. After receiving the feedback information within the time specified by the delay timer, the source node sends the data packet it generates to the optimal candidate relay node. Take the optimal candidate relay node as the relay node, and this relay node executes the same process as the source node to find the optimal candidate relay node until finally a surface sink node is found within the communication range, realizing the data transmission from the source node to the surface sink node; where The source node uses the fuzzy logic method combined with metric indicators to calculate the food concentration corresponding to each food node in the food node set, including: Select the packet delivery rate, forward diffusion-backward deviation, relative movement speed, distance progress, and node remaining energy as metric indicators. Select the membership function of the metric indicators. The source node performs fuzzification-defuzzification according to the membership function of the metric indicators and the fuzzy rule definition, and calculates the food concentration corresponding to each food node in the food node set; where a membership function combining a triangle and a trapezoid is selected as the membership function of the metric indicators; correspondingly, The packet delivery rate includes two membership types: low and high; where low means the delivery rate is lower than 30%; high means the delivery rate is higher than 70%. The forward diffusion-backward dissociation deviation includes three membership types: close, moderate, and deviated; where "close" represents the sum of the forward diffusion transmission angle and the backward dissociation difference angle ; "deviated" represents the sum of the forward diffusion transmission angle and the backward dissociation difference angle ; "moderate" represents the sum of the forward diffusion transmission angle and the backward dissociation difference angle ; The relative motion speed includes three membership types: slow, moderate, and fast; among them, slow means that the relative motion speed of the node is lower than 0.2 , represents the maximum speed of the node; moderate means that the relative motion speed of the node is equal to 0.5 ; fast means that the relative motion speed of the node is higher than 0.8 ; The distance progress includes two membership types: large and small. Among them, large means the distance progress is greater than 0.8 , represents the communication radius of the node; small means the distance progress is less than 0.2 ; The remaining energy of the node includes three membership types: high, medium, and low. Among them, high indicates the remaining energy of the node , represents the initial energy of the node; medium indicates the remaining energy of the node ; low indicates the remaining energy of the node .

2. The security routing method based on the fuzzy logic-fish swarm search algorithm according to claim 1, characterized in that, The source node determines whether there are food nodes in the water area based on the beacon auxiliary information sent by itself. If not, the source node discards the data packet it generates.

3. The security routing method based on fuzzy logic - fish school search algorithm according to claim 1, characterized in that The optimal candidate relay node determines whether there are food nodes in the water area based on the beacon auxiliary information sent by itself. If not, the optimal candidate relay node does nothing.

4. The security routing method based on fuzzy logic - fish - school search algorithm according to claim 1, characterized in that If the source node does not receive the feedback information within the time specified by the delay timer, it deletes the optimal candidate relay node from the food node set. The source node re-uses the fish swarm algorithm to find the food node with the highest food concentration from the remaining food nodes in the food node set as the new optimal candidate relay node. The source node sends request control information to the optimal candidate relay node and sets a delay timer at the same time. The new optimal candidate relay node determines whether there are food nodes in the water area based on the beacon auxiliary information sent by itself. If there are, it sends feedback information to the source node. After receiving the feedback information within the time specified by the delay timer, the source node sends the data packet it generates to the optimal candidate relay node. The new optimal candidate relay node is used as the relay node, and this relay node performs the same process as the source node to find the optimal relay node until finally a water surface convergence node is found within the communication range, realizing the data transmission from the source node to the water surface convergence node.

5. The security routing method based on the fuzzy logic-fish school search algorithm according to claim 1, wherein The fuzzy rules are defined as fuzzy descriptions of the corresponding link quality according to the membership degree type combinations corresponding to the packet delivery rate, forward diffusion-backward deviation, relative movement speed, distance progress, and node remaining energy respectively.

6. The security routing method based on fuzzy logic - fish school search algorithm according to claim 1, characterized in that The source node performs fuzzification - defuzzification according to the membership degree function of the metric index and the fuzzy rule definition, and calculates the food concentration corresponding to each food node in the food node set, including: Calculating the membership degree values of the five metric indexes corresponding to each food node in the food node set according to the membership degree function of the metric index; Performing fuzzy logic reasoning according to the fuzzy rule definition and using the IF / THEN rule; Defining the membership degree function of the link quality, and realizing the defuzzification operation by using the area centroid method to calculate the link quality value between the source node and each food node; Evaluating the food concentration corresponding to each food node in the food node set according to the link quality value.

7. The security routing method based on fuzzy logic - fish swarm search algorithm according to claim 1, characterized in that Each sensor node is equipped with an acoustic - optical communication device; Sending beacon auxiliary information through the acoustic device; Realizing the transmission of data packets, request control information, and feedback information between sensor nodes and between sensor nodes and the water surface convergence node through the optical communication device.

8. The security routing method based on the fuzzy logic - fish - school search algorithm according to claim 1, wherein The food node is a sensor node that has a positive forward progress and the remaining energy exceeds the energy threshold determined according to the distance progress metric index.

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