## 6 Types and Challenges of NCS
### Challenges of NCS
#### 6.2.2 Network-Induced Delay and Packet Dropout  
In the literature, many delay induced network models have been proposed [48; 49; 60; 70; 84; 86; 98; 99; 100; 118]. In Reference [84], four main delay models were discussed, namely: (a) constant delay model, (b) stochastic delay model, (c) Markov chain model, and (d) hidden Markov model. Reasons for these types of delays are: limited bandwidth, network traffic, and transmission protocols [99]. Two types of delays mainly occur: (i) sensor to-the controller delays and (ii) controller to-the actuator delays. Since network-induced delays depend on the networking circumstances [60], they are sometimes time-varying, unpredictable, and the upper bound is unknown. As a consequence, network-induced delays are ordinarily sculptured as internal time-varying delay [48; 100] and a Markov chain with known transition chances [49; 86], with partially transition chances [89] and with arbitrary shift [70]. As mentioned, in Reference [98], network-induced delays have been seen as a cause of degradation of performance of the system or possibly the cause for system instability. In Reference [118] authors proposed an algorithm based on the gradient push-sum method to solve the Electronic Data Processing (EDP) in a distributed manner over the communication networks with time-varying topologies and communication delays. This proposed algorithm is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. There are some systems where the presence of communication delay may have a positive effect on the system performance as in Reference [119]. In Reference [151] authors proposed LMI approach to determine the two-mode-dependent static output feedback controller gains to compensate for the random network-induced delays efficiently and provide the desired control performance. Table 7 shows the quantitative data for the delay range considered in the different studies carried out in the literature. The highest delay considered was 6 s [66]. Data-packet dropout is a vital issue, which is due to the defective transmission pathway. Limited bandwidth and bulk data transfer over one line are responsible for this defective transmission. Many studies have considered packet losses in the NCSs [41; 63; 69; 75; 88; 92; 94; 109; 113; 114]. These issues often occur because of exchanging the data amongst the various devices, which degrades the performance and can destabilize the system. Due to traffic congestion, the data-packet loss is also a major concern. Mostly, the dropout effect is also known as a Bernoulli or Markov process. In most communication networks, different data packets suffer different delays, which produces a situation where a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Type of Sampling** &
\begin{tabular}{c} **Sampling Interval/Sampling** \\ **Interval Range (_in ms_)** \\ \end{tabular} \\ \hline
[1] & Aperiodic & 16 to18 \\ \hline
[4] & Periodic & 40, 80 \\ \hline
[40] & Periodic & 22.1 \\ \hline
[50] & Periodic & 785 \\ \hline
[65] & Periodic & 132.77 \\ \hline
[77] & Aperiodic & 10 to 11 \\ \hline
[80] & Aperiodic & 104 to 169 \\ \hline
[93] & Periodic & 40 \\ \hline
[96] & Aperiodic & 30 to 1380 \\ \hline
[103] & Aperiodic & 0 to 299 \\ \hline
[119] & Aperiodic & 4 to 4.7 \\ \hline
[137] & Periodic & 100 \\ \hline
[146] & Aperiodic & 20 to 100 \\ \hline
[149] & Periodic & 1730 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sampling rate considered in different studies. data packet sent earlier may arrive at the destination later, or vice versa; see Figure 13[46]. This phenomenon is referred to as data packet disorder. Table 8 shows the quantitative data of the packet loss rate considered in the different studies existing in the literature. The highest packet loss rate considered was 80% in Reference [63].