## 4 Results
### Jamming Detection Algorithm Use Case
Figure 18: _Data threshold_ decisions. now analyzed. Step 12: (\(n=2\)): Data = Data 2, where Data 2 = 95.5 % (from step 6). Rhombs from Figure 18 are used to decide from steps 14 to 19 again:  
Step 14: First rhomb: Data 2 (95.5) \(<=Th_{A_{B2}}\) (94.5) \(\Rightarrow\)_no_. Step 15: Second rhomb \(Th_{A_{B2}}(94.5)\)\(<Data2(95.5)\)\(<=Th_{B_{C2}}(95)\)\(\Rightarrow\)_no_. Step 16: Third rhomb \(Th_{B_{C2}}(95)\)\(<Data2(95.5)\)\(<=Th_{C_{D2}}(96)\)\(\Rightarrow\)_yes_. Step 17: Fourth rhomb \(Th_{C_{D2}}(96)\)\(<Data2(95.5)\)\(<=Th_{D_{E2}}(98)\)\(\Rightarrow\)_no_. Step 18: \(Th_{D_{E2}}(98)\)\(<Data2(95.5)\)\(\Rightarrow\)_no_, then:  
Step 19: Obtain _data label_ associated to Data selected (see Figure 19). Step 20: Is _data label_\(=C_{0}?\)\(\Rightarrow\)_no_. Step 21: Is _data label_\(=D_{E}?\)\(\Rightarrow\)_no_. Step 22: Is _data label_\(=R_{A}?\)\(\Rightarrow\)_yes_. Step 23: Is _data label_\(=R_{E}?\)\(\Rightarrow\)_no_. Step 24: Is _data label_\(=N_{I}?\)\(\Rightarrow\)_no_. Step 25: \(C_{C_{O}}=0\). Step 26: \(C_{D_{E}}=0\). Step 27: \(C_{R_{A}}=1+1=\mathbf{2}\). Step 28: \(C_{R_{E}}=0\). Step 29: \(C_{N_{I}}=0\). Step 30: \(n=n+1=2+1=3\). Step 31: RSSI analyzed \((n>3)?\)\(\Rightarrow\)_no_. Step 32: PDR analyzed \((n>2)?\)\(\Rightarrow\)_yes_. RSSI analysis: Next, steps 14 to 32 are repeated, but this time \(n=3\), so the RSSI is now analyzed. Step 13: (\(n=3\)): Data = Data 3, where Data 3 = \(-86\) dBm (from step 6). Rhombs from Figure 18 are used to decide from steps 14 to 19 again:  
Step 14: First rhomb: Data 3 \((-86)\)\(<=Th_{A_{B3}}\)\((-102.5)\)\(\Rightarrow\)_no_. Step 15: Second rhomb \(Th_{A_{B3}}(-102.5)\)\(<\)\(Data3(-86)\)\(<=Th_{B_{C3}}(-94.5)\)\(\Rightarrow\)_no_. Step 16: Third rhomb \(Th_{B_{C3}}(-94.5)\)\(<\)\(Data3(-86)\)\(<=Th_{C_{D3}}(-85)\)\(\Rightarrow\)_yes_. Step 17: Fourth rhomb \(Th_{C_{D3}}(-85)\)\(<\)\(Data3(-86)\)\(<=Th_{D_{E3}}(-76.5)\)\(\Rightarrow\)_no_. Step 18: \(Th_{D_{E3}}(-76.5)\)\(<\)\(Data3(-86)\)\(\Rightarrow\)_no_, then:  
Step 19: Obtain _data label_ associated to Data selected (see Figure 19). Step 20: Is _data label_\(=C_{0}?\)\(\Rightarrow\)_no_. Step 21: Is _data label_\(=D_{E}?\)\(\Rightarrow\)_no_. Step 22: Is _data label_\(=R_{A}?\)\(\Rightarrow\)_yes_. Step 23: Is _data label_\(=R_{E}?\)\(\Rightarrow\)_no_. Step 24: Is _data label_\(=N_{I}?\)\(\Rightarrow\)_no_. Step 25: \(C_{C_{O}}=0\). Step 26: \(C_{D_{E}}=0\). Step 27: \(C_{R_{A}}=2+1=\mathbf{3}\). Step 28: \(C_{R_{E}}=0\). Step 29: \(C_{N_{I}}=0\). Step 30: \(n=n+1=3+1=\mathbf{4}\). Step 31: RSSI analyzed \((n>3)?\)\(\Rightarrow\)_yes_. Step 32: _Does not apply as (\(n>3\))_. Once the three performance metrics have been analyzed, we move on to the final part of the algorithm (Figure 20). In this example, the following results are obtained:  
Step 33: Display the result of the counters: \(C_{C_{O}}=0\), \(C_{D_{E}}=0\), \(C_{R_{A}}=\mathbf{3}\), \(C_{R_{E}}=0\) and \(C_{N_{j}}=0\). Step 34: Is there a Counter \(=37\Rightarrow yes\). Step 35: Because \(C_{R_{A}}=3\) then there is _Random Jammer_ in the network. Step 36: Repeat the process? \(\Rightarrow\)\(no\). End. The proposed mechanism for the detection of jamming has been validated; thanks to these results, we can extrapolate jamming detection to other types of industries such as maintenance and smart manufacturing. The lessons learned, as well as their impact on safety and reduction of operating and maintenance costs, are discussed below.