Research Article
[Retracted] A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things
| (1) | Input: Analysis of neighbors nodes in IoTs (N_v ∈ IoTs) | | (2) | Output: Elimination of redundant data based on neighbors nodes in IoTs (N_v ∈ IoTs) | | (3) | begin | | (4) | Ordinary nodes N_n = {0, 1, 2, 3, N_n} with MAC address value and location | | (5) | Clusters K_n = {K_1, K_2. . . K_n_ - _1} | | (6) | While every k_i ∈ IoTs do | | (7) | Generate a message | | (8) | Set a join request value 1 | | (9) | Broadcast message | | (10) | end while | | (11) | While every Node(i) ∈ IoTs do | | (12) | If RSSI(k_i ≥ K_i + 1.....n) then | | (13) | Update the message | | (14) | Set destination K_i | | (15) | Backoff Timer = rand(20–1000 milliseconds) | | (16) | Re-broadcast message | | (17) | end if | | (18) | end while | | (19) | While Nodes ∈ k_i do | | (20) | Calculate Euclidean distance(Ed) among all nodes | | (21) | If (Ed of Node(i)and(j) is ≤ td) then | | (22) | Both nodes I and j are neighbors | | (23) | Check data redundancy among | | (24) | Eliminate redundant data captured by node | | (25) | else | | (26) | Both nodes I and j are not neighbors | | (27) | end if | | (28) | end while | | (29) | While Node(i) ∈ K_i do | | (30) | If Node(i).data ≤ threshold then | | (31) | Aggregate data using k-mean or SVM | | (32) | Send aggregate data to the Base station | | (33) | else | | (34) | Discard data | | (35) | end if | | (36) | end while |
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