	US 11,956,372 B2
	Judgment method for edge node computing result trustworthiness based on trust evaluation
Min Wei, Chongqing (CN); Er Xiong Liang, Chongqing (CN); and Ping Wang, Chongqing (CN)
Assigned to CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS, Chongqing (CN)
Appl. No. 17/418,250
Filed by CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS, Chongqing (CN)
PCT Filed Jul. 15, 2020, PCT No. PCT/CN2020/102198 § 371(c)(1), (2) Date Jun. 25, 2021, PCT Pub. No. WO2021/237898, PCT Pub. Date Dec. 2, 2021.
Claims priority of application No. 202010469593.1 (CN), filed on May 28, 2020.
Prior Publication US 2022/0321355 A1, Oct. 6, 2022
Int. Cl. H04L 9/32 (2006.01); H04L 9/40 (2022.01)

CPC H04L 9/3247 (2013.01) [H04L 9/3239 (2013.01); H04L 63/0236 (2013.01); H04L 63/0281 (2013.01)]

1 Claim

1. A judgment method for edge node computing result trustworthiness based on trust evaluation, characterized in that the method comprises following steps:

S1 Before Network Operation: each edge node to be added transmits identity information ID_Aito an edge proxy for registration, a security administrator sets an error rate ER_Aiof computing errors allowed to be caused by each edge node in an industrial production environment; the edge proxy marks evidence collected for which number of times with ε (ε=1, 2, . . . , CN_τ), and marks trust associated information as a trust value computed in which round with τ (τ∈N), where τ=0 when an initial trust value is evaluated, τ≥1 when a trust value is updated; CN_τ represents a total number of times of evidence collection required when the edge proxy computes a trust value in a τ^thround, and t_τ represents time when the edge proxy starts computing the trust value in the τ^thround; and after verifying an identity of an edge node, the edge proxy starts evaluating an initial trust value of the edge node;

S11 Evidence Collection: the edge proxy starts evaluating the initial trust value of the edge node at t₀, the edge proxy randomly generates a to-be-computed data set Data_B-0^c-ε={a_0-0^ε, a_1-0^ε, a_2-0^ε, . . . , a_l-0^ε}, and generates a result set Data_B-0^r-ε={b_1-0^ε, b_2-0^ε, b_3-0^ε, . . . , b_l-0^ε} after adjacent data are pairwise computed as a reference set, wherein this solution specifies that the number of times of evidence collection CN₀required for initial trust value evaluation is 3;

the edge proxy transmits a to-be-computed set to the edge node, the edge node computes and then transmits a computing result set Data_Ai-0^r-ε={c_1-0^ε, c_2-0^ε, c_3-0^ε, . . . , c_l-0^ε} and a computing result Hash value set Data_Ai-0^r-ε={h_1-0^ε, h_2-0^ε, h_3-0^ε, . . . , h_l-0^ε} to the edge proxy;

according to the computing result set Data_Ai-0^r-ε from the edge node, the edge proxy computes a Hash value Data_Ai-0^h′-ε={h_1-0^ε′, h_2-0^ε′, h_3-0^ε′, . . . , h_l-0^ε′} corresponding thereto;

the edge proxy records time t_B-0^s-ε when the to-be-collected set is transmitted for the ε time, time t_B-0^r-ε when computing is completed, and time t_Ai-0^r-ε when a computing result of an edge node Ai is received, where i represents a number of nodes, i=1, 2, . . . , n;

S12 Evidence Processing: the edge proxy processes collected data as follows:

(S12-1) accuracy represents a proportion of the number of correct computing results to the number of total data; an accuracy computing formula of evidence collection for the ε^thtime is as follows:

where N_Ai-τ^ac-ε represents identical number in a result set Data_B-τ^r-ε computed by the edge proxy in evidence collection for the ε^thtime and a result set Data_Ai-τ^r-ε computed by the edge node Ai, ε represents evidence collected for the ε^thtime, τ represents trust computing in the τ^thround, and l represents a data amount in evidence collection each time;

(S12-2) integrity represents a proportion of the number of complete data to the number of total data; an integrity computing formula of evidence collection for the ϑ^thtime is as follows:

where N_Ai-τ^cm-ε represents identical number in a result Hash value set Data_Ai-ε^h′-ε computed by the edge proxy in evidence collection for the ε^thtime and a result Hash value set Data_Ai-τ^h-ε computed by the edge node Ai, ε represents evidence collected for the ε^thtime, τ represents trust computing in the τ^thround, and l represents a data amount in evidence collection each time;

(S12-3) timeliness represents a difference between a computing efficiency of the edge node Ai and a computing efficiency of the edge proxy; a timeliness computing formula of evidence collection for the ε^thtime is as follows:

E_Ai-τ^tm-ε=T_Ai-τ^tm-ε−T_B-τ^tm-ε (3)

where T_Ai-τ^tm-ε represents a computing efficiency of the edge node in evidence collection for the ε^thtime; T_B-τ^tm-ε represents a computing efficiency of the edge proxy in evidence collection for the ε^thtime, and τ represents trust computing in the τ^thround;

when the edge proxy evaluates the initial trust value, the edge proxy computes a computing efficiency

of CN₀edge nodes and a computing efficiency

of the edge proxy according to the time t_B-0^s-ε when the to-be-collected set is transmitted for ε^thtime, the time t_B-0^r-ε when computing of the to-be-computed set is completed, and the time t_Ai-0^r-ε when a computing result of an edge node Ai is received, substitutes the results into formula (3), and computes to obtain timeliness of the CN₀edge nodes Ai;

when evaluating the initial trust value, the edge proxy processes evidence collected for three times by means of formula (1), formula (2) and formula (3), to obtain three values of each of accuracy, integrity and timeliness of the edge nodes Ai to be added;

S13 Initial Trust Evaluation:

(S13-1) Computing Initial Trust Value:

a direct trust value is a quantization value that indicates an ability of the edge node to complete a request task and is based on an interactive record history between the edge proxy and the edge node; when the edge proxy computes the initial trust value of the edge node, the edge node is in a to-be-operated state; the edge proxy conducts fuzzy evaluation on direct trust factors of edge nodes to be operated respectively, steps of computing direct trust values are as follow:

(S13-1-a) determining a factor set E={E_Ai-τ^ac-ε, E_Ai-τ^cm-ε, E_Ai-τ^tm-ε}, and an evaluation set V={V₁, V₂, V₃}, where V₁represents untrustworthiness, V₂represents uncertainty, and V₃represents trustworthiness; it is stipulated that μ^unis an untrustworthy membership degree, and 0≤μ^un<β_u; μⁱⁿis an uncertain membership degree, and β_u≤μⁱⁿ<β_c; μ^cris a trustworthy membership degree, and β_c≤μ^cr≤1, where β_uis untrustworthy threshold and β_cis trustworthy threshold; the edge proxy computes membership degrees of accuracy, integrity and timeliness, computing formulae are as follows:

S13-1-a-1 computing formula of membership degree of accuracy in evidence collection for the ε^thtime is as follows:

S13-1-a-2 computing formula of membership degree of integrity in evidence collection for the ε^thtime is as follows:

S13-1-a-3 computing formula of membership degree of timeliness in evidence collection for the ε^thtime is

(S13-1-b) computing proportions of membership degrees corresponding to accuracy, integrity and timeliness in trust computing in the τ^thround belonging to V₁, V₂, V₃, which are {r_11-τ, r_12-τ, r_13-τ}, {r_21-τ, r_22-τ, r_23-τ} and {r_31-τ, r_32-τ, r_33-τ} respectively, for example,

where N(μ_1-τ^un-ε) represents the number of accuracy membership degrees of CN_τ accuracy membership degrees within an untrustworthy membership degree range; the edge proxy obtains a judgment matrix

(S13-1-c) computing weight corresponding to accuracy, integrity and timeliness using the entropy weight method, the computing steps being as follows:

S13-1-c-1 forming a matrix by CN_τ membership degrees μ_1-τ^ε, μ_2-τ^ε, μ_3-τ^ε corresponding to accuracy, integrity and timeliness:

S13-1-c-2 computing information entropy corresponding to accuracy, integrity and timeliness:

S13-1-c-3 computing weight corresponding to accuracy, integrity and timeliness:

in order to avoid the condition where the weight is zero when the degree of dispersion of a certain factor is too small, weight ranges corresponding to accuracy, integrity and timeliness are α₁∈[0.5,0.8], α₂∈[0.01,0.2] and α₃∈[0.2,0.4] respectively, where α₁>α₃>α₂; when the weight obtained using the entropy weight method is not within the specified range, the maximum or minimum value of the corresponding range is taken, actual weight is

(S13-1-d) computing a judgment result Z_Ai-τ=A_τ*R_τ={z_1-τ, z_2-τ, z_3-τ}, there being following three cases:

S13-1-d-1 when z_1-τ is the maximum, the edge node Ai is untrustworthy, the edge proxy does not compute an average membership degree of accuracy, integrity and timeliness;

S13-1-d-2 when z_2-τ is the maximum, the edge node Ai is uncertain in trust, the edge proxy computes means of membership degrees within a range [β_u, β_c) corresponding to accuracy, integrity and timeliness, which are

where a denominator represents the number of membership degrees of all factors within the range [β_u, β_c) and a numerator represents the sum of membership degrees of all factors within the range [β_u, β_c); μ_i-τ^in-ε is the membership degree of the accuracy of the τ^thevidence collection within the range [β_u, β_c), μ_2-τ^in-ε is the membership degree of the integrity of the τ^thevidence collection within the range [β_u, β_c), μ_2-τ^in-ε is the membership degree of the timeliness of the τ^thevidence collection within the range [β_u, β_c);

S13-1-d-3 when z_3-τ is the maximum, the edge node Ai is trustworthy, the edge proxy computes means of membership degrees within a range [β_c, 1] corresponding to accuracy, integrity and timeliness, which are

where a denominator represents the number of membership degrees of all factors within the range [β_c, 1] and a numerator represents the sum of membership degrees of all factors within the range [β_c, 1]; μ_1-τ^cr-ε is the membership degree of the accuracy of the τ^thevidence collection within the range [β_c, 1], μ_2-τ^cr-ε is the membership degree of the integrity of the τ^thevidence collection within the range [_c, 1], μ_2-τ^cr-ε is the membership degree of the timeliness of the τ^thevidence collection within the range [β_c, 1];

(S13-1-e) the edge proxy computes a direct trust value Trust_Ai-τ^cdthe edge node Ai according to the average membership degree of accuracy, integrity and timeliness, and the weight thereof, the computing formula being as follows:

Trust_Ai-τ^cd=α_1-τ^cd=α_1-τ′μ_1-τ+α_2-τ′μ_2-T+α_3-τ′μ_3-τ (4)

since the edge nodes to be operated have no historical trust values and feedback scores, at this moment, the initial direct trust value is the final trust value, and the final trust value before the edge node Ai operates is Trust_Ai-0^u=Trust_Ai-0^cd;

(S13-2) Computing Trust Identifier: edge node trust is divided into three levels, namely an untrustworthy level, an uncertain level and a trustworthy level;

a threshold of the untrustworthy level is β_u, a threshold of the trustworthy level is β_c, 0<β_u<β_c≤1 and β_c=[1+10(ER_Ai)²]⁻¹, β_u=β_c−0.2, where ER_Airepresents an error rate of computing errors allowed to be caused by each edge node Ai in an industrial production environment occasionally due to mistake, 0≤ER_Ai<30%, the greater the β_uand β_c, the sensitive the system to incorrect computing results; the security administrator sets an error rate of errors allowed to be caused by each edge node in an industrial production environment, and the edge proxy computes corresponding β_uand β_caccording to the error rate;

the edge proxy computes a trust identifier of an edge node Ai to be operated according to a judgment result, the rules are as follows:

(S13-2-a) for an edge node of which the trust value level is a trustworthy level, in order to prevent a malicious node from cheating trust, the edge proxy replaces a trust value of the edge node with a trustworthy level with

that is, degrades the edge node with a trustworthy level to an edge node with an uncertain level;

(S13-2-b) for an edge node of which the trust value level is an uncertain level, the edge proxy allocates a trust identifier TI_Ai-0=1 of an initial trust value to the edge node, computes valid time T_Ai-0^vthe initial trust identifier according to formula (5), and stores initial trust associated information locally;

a computing formula of the valid time T_Ai-0^vthe trust identifier of the initial trust value is as follows:

T_Ai-0^v=5i×CN₀×T_Aι-0×l+5ΔT (5)

where i represents a number of on-line edge nodes, CN₀represents a number of times of evidence collection in initial trust evaluation, T_Aι-0 represents an average computing efficiency

of edge nodes Ai, l represents a data amount of evidence collection each time, ΔT represents a time interval between trust updates, and the valid time is in second; if a trust identifier of an edge node is expired, the edge proxy lists the edge node in a blacklist;

(S13-2-c) for an edge node of which the trust level is an untrustworthy level, that is, an edge node of which the z_1-τ is the maximum, to avoid evaluation errors, the edge proxy repeats the above-mentioned steps of evidence collection, evidence processing and trust evaluation to evaluate the initial trust value thereof twice, if the trust value is still untrustworthy after being evaluated twice, reports to the security administrator to replace the edge node, and computes an initial trust value of the replaced edge node to be added;

initial trust associated data of the edge node Ai includes initial trust value evaluation start time t₀, a node identity identifier ID_Ai, an accuracy membership degree mean μ_1-0, an integrity membership degree mean μ_2-0, a timeliness membership degree mean μ_2-0, an initial trust value Trust_Ai-0^u, a trust identifier TI_Ai-0and valid time T_Ai-0^v;

the edge proxy transmits the trust identifier to a site device, the site device checks the trust identifier of the edge node to be operated and then transmits data to an edge node with a trust identifier greater than 0, and then the edge node is in an operating state;

S2 After Network Operation:

S21 Evidence Collection: after a network operates for ΔT time, the edge proxy initiates an update trust request to the site device, the edge proxy starts to collect the collected data of the site device, a computing result of the edge node and a Hash value thereof, and a feedback score from the site device, records response time and a historical direct trust value of the edge node; after the edge proxy initiates the trust update request, the edge proxy conducts each evidence collection in following two cases:

case 1: the edge node directly returns the computing result to the site device, and the site device transmits the computing result of the edge node and the Hash value thereof to the edge proxy;

case 2: after preliminary computing, the edge node transmits the computing result and the Hash value thereof to the edge proxy, the edge proxy collects evidence and uploads the computing result of the edge node, trust identifier and signature to an industrial cloud, the industrial cloud checks the trust identifier of the edge node and verifies the signature and then further processes a preliminary computing result of the edge node, the industrial cloud transmits the computing result and the signature to the edge proxy, the edge proxy verifies the signature and then transmits the computing result to the site device;

the edge proxy collects evidence data in the above two cases, and collects l evidence data as one evidence collection; each round of trust update requires evidence collection for CN_τ times, and the edge node is in an operating state at this moment; the edge proxy records the number of times of evidence collection with ε (ε=1, 2, . . . , CN_τ); during the τ^thround of trust update, the edge proxy collects evidence for CN_τ times and then conducts evidence processing and trust update operations; a time interval between every two rounds of trust updates is ΔT; a computing formula of the number of times of evidence collection CN_τ required for the τ^thround of trust update is specified as follows:

CN_τ=[6×arctan[0.5×TI_Ai-(τ-1)]] (6)

the edge proxy computes the number of times of evidence collection CN_τ required for the τ^thround of trust update according to the trust identifier of the (τ−1)^thtime; the edge proxy rapidly updates the trust value of the edge node when the trust identifier is small and the number of times of evidence collection is less; at the initial stage of network operation, the number of times of evidence collection increases with the increase of the number of trustworthy times, in order to update the trust value in time and reduce trust computing amount, the number of times of evidence collection cannot be infinitely increased, and the maximum value of the number of times of evidence collection

(S21-1) Direct Trust Factor Collection:

the site device transmits the collected data α_0-τ^ε to the edge proxy and the edge node Ai simultaneity, the site device transmits a piece of data every Δt, and the edge proxy and the edge node start processing after receiving the second collected data; the edge proxy processes a computing result of the data collected in two consecutive times as b_ϑ-τ^ε, the edge node Ai processes a computing result of the data collected in two consecutive times as c_ϑ-τ^ε, ϑ represents a serial number of evidence collected in each evidence collection, (ϑ=1, 2, . . . , l); during each evidence collection, the site device needs to transmit (l+1) data, and the data transmitted by the site device form a set

Data_D-τ^c-ϑ={a_0-τ^ε,a_1-τ^ε,a_2-τ^ε, . . . ,a_1-τ^ε};

at t_T, the edge proxy starts the τ^thround of trust update, the edge proxy collects evidence for CN_τ times in total, l data evidence collected for the ε^thtime including a computing result Data_B-τ^r-ε={b_1-τ^ε, b_2-τ^ε, . . . , b_ϑ-τ^ε, . . . , b_1-τ^ε} of the edge proxy, a computing result Data_Ai-τ^r-ε={c_1-τ^ε, c_2-τ^ε, . . . , c_ϑ-τ^ε, . . . , c_1-τ^ε} of the edge node Ai and a Hash value Data_Ai-τ^h-ε={h_1-τ^ε, h_2-τ^ε, . . . , h_ϑ-τ^ε, . . . , h_1-τ^ε} thereof, and a corresponding Hash value Data_Ai-τ^h′-ε={h_1-τ^ε′, h_2-τ^ε′, . . . , h_ϑ-τ^ε′, . . . , h_1-τ^ε′} computed by the edge proxy according to the computing result set Data_Ai-τ^r-ε of the edge node Ai; the edge proxy records time t_D-τ^s-ε of transmitting the first data by the site device when collecting evidence for the ε^thtime, time t_B-τ^r-ε computing the l^thresult by the edge proxy, and time t_Ai-τ^r-ε computing the l^thresult by the edge node Ai;

(S21-2) Historical Direct Trust Value Collection:

because trust dynamically changes with time, in order to avoid malicious acts, the edge proxy uses a historical direct trust value to correct a direct trust value, and the edge proxy uses a sliding window to store the historical direct trust value so as to reduce the influence of the old direct trust value on the new direct trust value; each edge node has a sliding storage window, the larger the window, the more the storage and computing overhead, so a short and small sliding storage window can limit the amount of trust computing and improve the efficiency of trust evaluation;

the sliding storage window includes u panes, each pane retains a historical direct trust value, that is, a direct trust value before the τ^thround of trust update is stored in the sliding storage window; a direct trust value stored in the k pane is Trust_{Ai-(τ-u+k-1)}^cd; only when each pane has a direct trust value, the window begins to move, and moves one pane every time; a new direct trust value is added into the window after the trust is updated, while an expired direct trust value is extruded out of the window; during the τ^thround of trust update, direct trust values from the (τ−u)^thround of trust update to the (τ−1)^thround of trust update are stored in the window, and a direct trust value of the τ^thround is stored in the sliding storage window after the τ^thround of trust update; when a trust identifier of the edge node Ai is equal to 0, the edge node is regarded as a malicious node, and the edge proxy deletes a sliding storage window thereof,

(S21-3) Feedback Score Collection:

the edge proxy updates a final trust value of an edge node in an operating state and also needs to take into account a feedback score given to a computing result of the edge node by the site device; a rule of giving scores to edge nodes by the site device is as follows: if a safety accident occurs, the site device feeds back d_ϑ-τ^ε=−1 regardless whether a trust update is being conducted, and the edge proxy lists an edge node corresponding to the feedback score in a blacklist; otherwise, the site device feeds back scores given to computing results: bad review d_ϑ-τ^ε=0 and good review d_ϑ-τ^ε=1;

the site device feeds back the scores given to the computing results to the edge proxy, during the τ^thround of trust update, the edge proxy collects for CN_τ times and collects l feedback scores each time, and a feedback score collected by the edge proxy for the ε^thtime is Data_Ai-τ^f-ε={d_1-τ^ε, d_2-τ^ε, . . . , d_ϑ-τ^ε, . . . , d_l-τ^ε} including scores given, by the site device, to computing results directly returned by v edge nodes to the site device and scores given, by the site device, to computing results transmitted by (l−v) edge nodes to the industrial cloud for processing and then returned to the site device; a proxy signature based on elliptic curve is used to make the communication between the edge nodes and the industrial cloud trustworthy, no matter the computing results received by the site device come from the edge nodes or the industrial cloud, the objects to which the site device feeds back scores are edge nodes;

S22 Evidence Processing:

(S22-1) Direct Trust Factor Processing:

after collecting evidence for CN_τ times, the edge proxy respectively computes accuracy, integrity and timeliness of an edge node Ai during each evidence collection in the τ^thround of trust update;

(S22-1-a) the edge proxy computes accuracy of the edge node Ai according to formula (1);

(S22-1-b) the edge proxy computes integrity of the edge node Ai according to formula (2);

(S22-1-c) according to the time t_D-τ^s-ε of transmitting the first data by the site device when collecting evidence for the ε^thtime, the time t_B-τ^r-ε of computing the l^thresult by the edge proxy, and the time t_Ai-τ^r-ε computing the l^thresult by the edge node Ai, the edge proxy computes a computing efficiency

of the edge node and a computing efficiency

of the edge proxy, substitutes T_Ai-τ^tm-ε, T_B-τ^tm-ε into formula (3), and compute timeliness of the edge node Ai;

when conducting the τ^thround of trust update, the edge proxy processes the collected direct trust factors by means of formula (1), formula (2) and formula (3), to obtain CN_τ values of each of accuracy, integrity and timeliness of an edge node Ai to be examined;

(S22-2) Historical Trust Value Processing:

for weight factors of historical direct trust values at different time, there is a need to take into account a time factor, that is, the longer the time of the trust value, the lower the proportion; a weight of the k^thpane of the sliding storage window is:

φ_k=e^−ρ(u-k) (7)

where ρ represents an attenuation coefficient which is 0.3; if the sliding storage window is not fully stored, u is the number of actual historical direct trust values;

according to the historical direct trust value and weight thereof in the sliding storage window, the edge proxy computes a weighted average historical trust value Trust_Ai-τ^hdof the edge node Ai in the τ^thround of trust update:

(S22-3) Feedback Score Processing:

for an edge node with a feedback score of −1, the security administrator replaces the edge node with an edge node to be added, and the edge proxy repeats an initial trust value computing step, to evaluate an initial trust value of the edge node to be added;

according to the feedback score, the edge proxy computes reward and penalty factors of the edge node Ai in the τ^thround of trust update; and according to a difference ΔN_Ai-t^ε=N_Ai-τ^g-ε−└l×(1−ER_Ai)┘ between the total number of times of good review N_Ai-τ^g-ε during evidence collection for the ε^thtime and the minimum required number of correct computing results, the edge proxy computes a reward factor E_Ai-τ^g-ε and a penalty factor E_Ai-τ^b-ε corresponding to the evidence collection for the ε^thtime, where N_Ai-τ^g-ε=Σ_ϑ=1^ld_ϑ-τ^ε, ER_Aiis the error rate that the edge node is allowed to calculate in an industrial production environment;

if ΔN_Ai-τ^ε≥0, the reward factor and penalty factor corresponding to the evidence collection for the ε^thtime are E_Ai-τ^g-ε=0.3[(1+e^−ΔN^_Ai-τ^{^ε})⁻¹−0.5] and E_Ai-τ^b-ε=0 respectively; otherwise, the reward factor and penalty factor corresponding to the evidence collection for the ε^thtime are E_Ai-τ^g-ε=0 and E_Ai-τ^b-ε=0.4[(1+e^−ΔN^_Ai-τ^{^ε})⁻¹−0.5] respectively; the reward degree is small and the penalty degree is large, which reflects the characteristic that the trust value is slowly increased and quickly decreased;

the edge proxy computes a final reward or penalty factor E_Ai-τ^faccording to the reward and penalty factors in the τ^thround of trust update:

good feedback from the field device increases the trust value of the edge node Ai and bad feedback rapidly decreases the trust value of the edge node Ai; if there is safety accident feedback from the site device, E_Ai-τ^fappears as a penalty factor, E_Ai-τ^f=0; if there is no safety accident feedback, E_Ai-τ^f>0 represents reward, E_Ai-τ^f<0 represents penalty, and E_Ai-τ^f=0 represents neither reward nor penalty;

S23 Trust Update:

according to the direct trust value, historical trust value and feedback scores, the edge proxy updates the trust value of the edge node, the edge node is in a to-be-examined state at this moment; a time interval between every two rounds of trust updates is ΔT;

(S23-1) Computing Direct Trust Value:

the edge proxy repeats the step of computing a direct trust value when evaluating initial trust, and computes a direct trust value Trust_Ai-τ^cdof the edge node Ai to be examined of which the judgment result is trustworthy or uncertain in the τ^thround of trust update by means of formula (4); for an edge node to be examined of which the judgment result is untrustworthy, the edge proxy directly lists the edge node in a blacklist;

(S23-2) Correcting Direct Trust Value:

before computing the final trust value, the edge proxy needs to correct the direct trust value by using the weighted average historical direct trust value; the edge proxy weights and aggregates Trust_Ai-τ^cdand Trust_Ai-τ^hdof the edge node Ai to obtain a corrected direct trust value Trust_Ai-τ^dthe edge node Ai in the τ^thround of trust update:

Trust_Ai-τ^d=δ×Trust_Ai-τ^cd+(1−δ)×Trust_Ai-τ^hd (10)

where δ is used to balance proportions of current trust and historical trust, and δ is defined as follows:

where 0<δ₁<δ₂<1, it is specified that δ₁=0.3, δ₂=0.7, the value of δ₁is small, to prevent the edge node from accumulating trust thereof quickly, and the value of δ₂is large, which reflects a penalty for a malicious act of the edge node;

(S23-3) Updating Final Trust Value:

according to the reward or penalty factor computed in formula (9), the edge proxy computes a final trust value of the edge node to be examined;

a computing formula of the final trust value Trust_Ai-τ^uof the edge node Ai in the τ^thround of trust update is as follows:

if a certain feedback score is −1, the final trust value of the edge node Ai in the τ^thround of trust update is equal to 0; otherwise, the final trust value of the edge node Ai in the τ^thround of trust update is equal to a corrected direct trust value of the edge node Ai plus a reward or penalty factor;

(S23-4) Computing Trust Identifier:

after trust update, the edge proxy compares the final trust value of the edge node to be examined with a trust value in Table 2-trust level table, and then computes a trust identifier of the edge node Ai according to the judgment result and the final trust value, rules are as follows:

TABLE 2

Trust Level Table

	Trust	Trust Value
Trust Level	Description	Range

1	Untrustworthy	[0, β_u)
2	Uncertain	[β_u, β_c)
3	Trustworthy	[β_c, 1]

(S23-4-a) for an edge node of which the trust value level is a trustworthy level, the edge proxy computes a trust identifier TI_Ai-τ thereof according to formula (13), computes valid time T_Ai-τ^vthe trust identifier according to formula (14), and then stores trust associated information thereof locally according to a data structure in Table 6;

TABLE 6

Trust Associated Data of Edge Node Ai

		Accuracy	Integrity	Timeliness	Corrected
	Node	membership	membership	membership	direct	Reward	Final
	identity	degree	degree	degree	trust	and	trust	Trust	Valid
Time	identifier	mean	mean	mean	value	penalty	value	identifier	time

t_τ	ID_Ai	μ_1−τ	μ_1−τ	μ_1−τ	Trust_Ai−τ^d	E_Ai−τ^f	Trust_Ai−τ^u	TI_Ai−τ	T_Ai−τ^v

a specific computing formula of the trust identifier of the edge node Ai in the τ^thround of trust update is as follows:

a computing formula of the valid time T_Ai-τ^vof the trust identifier of the trust value is as follows:

T_Ai-τ^v=6×CN_τ×l×(T_Aι-τ+Δt)+TI_Ai-τ×ΔT (14)

where CN_τ represents a number of times of evidence collection required in the τ^thround of trust update, l represents a data amount of evidence collection each time, T_Aι-τ represents an average computing efficiency

of edge nodes Ai, Δt represents a time interval at which the site device transmits data, ΔT represents a time interval between trust updates, and the valid time is in second; if a trust identifier of an edge node is expired, the edge proxy lists the edge node in a blacklist;

(S23-4-b) for an edge node of which the trust level is an uncertain level, a trust identifier thereof is unchanged; the edge proxy checks the trust identifier thereof, and if the number of times of continuous equality of the trust identifier is less than 3, the edge proxy allows the edge node to operate; otherwise, the edge proxy lists the edge node in a blacklist, and then the edge node is in an isolation state;

(S23-4-c) for an edge node of which the trust level is an untrustworthy level, the edge proxy lists the edge node in a blacklist directly, and then the edge node is in an isolation state; the edge proxy broadcasts identity information about the edge node in the blacklist and a trust identifier 0 thereof, and reports to the security administrator to replace the edge node; after the security administrator replaces the isolated edge node with an edge node to be added, the edge proxy repeats an initial trust value computing step, to separately evaluate an initial trust value of the edge node to be added;

trust associated data of the edge node Ai includes start time t_τ of the τ^thround of trust update, a node identity identifier ID_Ai, an average accuracy membership degree μ_1-τ, an integrity membership degree mean μ_2-τ, a timeliness membership degree mean μ_3-τ, a corrected direct trust value Trust_Ai-τ^d, a reward or penalty factor E_Ai-τ^f, a final trust value Trust_Ai-τ^u, a trust identifier TI_Ai-τ and valid time T_Ai-τ^v;

the edge proxy transmits the trust identifier to the site device, the site device decides whether to transmit data according to the trust identifier of the edge node, and transmits data to an edge node with a trust identifier greater than 0 rather than to an edge node with a trust identifier equal to 0;

after ΔT time, the edge proxy repeatedly executes the steps of evidence collection, evidence processing and trust update.