## 3 Internet of Things in Predictive Maintenance  
Converging the internet and sensing network technology, after the introduction of Radio Frequency Identification and the Wireless Sensor Networks, the IoT concept has been growing and attracting both in industries and academia since 2000, with the goal of connecting distant remote objects (anything or anyone) at any time in any place by involving different hardware devices, software, and communication capability in order to make any object intelligent, so that they can communicate virtually, regardless of physical location [25; 26; 27; 28]. The IoT ability to connect physical objects and allow them to share information through the internet may facilitate collecting a great amount of data that are the powerful strength for the success of any business and future prediction [29; 30]. In the maintenance world, embedded hardware that is made up by sensors and other smart devices powered by IoT is today reshaping the industrial and manufacturing maintenance processes [31; 32]. The internal equipment outfit is generally unobservable; thus, regular planned preventive maintenance do not provide enough or clear information involving the equipment status to maintainers, when it is operating without any physical symptom to depreciation, it is not practically easy to identify  
Figure 1: Industrial revolution maintenance techniques and objectives. whether there is any root to cause future defect. Consequently, the routine schedule is retained and sudden downtime may occur at an unexpected time, perhaps even during a heavy works period. The equipment gradually degrades overtime before reaching a complete collapse [33]. The insufficient maintenance accuracy of asset's conditions results in a thorough deterioration that reflect in service deficiency, clients' appointments postponement, and dismay while waiting for the procurement of new spare parts or equipment replacement that always goes together with an increase in cost demand. Thus, with the power of IoT, the predictive maintenance approach may be powerful in this game to integrate the direct monitoring of equipment through collecting continuous real time data from its health physical parameters. Late literature reviewed the IoT relevant concepts, technologies, architectures, services, applications, and business models [26; 29; 31; 34]. Among the applications, IoT offers predictive maintenance solutions in different scenarios from different industries; among them are healthcare industry [35; 36; 37; 2], energy [38; 39; 40; 41; 42; 43; 44], and industrial automation and machinery [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72], to which also this work belongs. The PdM Structure using IoT involves sensors for capturing the continuous real data of highlighted key stress factors that lead to deficiencies, such as vibration, temperature, noise level, pressure, power consumption, etc., and other coupled devices to make an asset visibility to the user by getting continuous sights on their health changes. The predictive maintenance using IoT cannot be efficient without advanced data analytics tools and machine learning techniques [73; 74; 75] that use historical performance data to discover the remarkable insights that are linked to the equipment performance, detect anomalies in variances, discover patterns or warning signals that could be a sign of impending failure, and from them:  
* estimate when the asset is probable to fail;
* classify the equipment's part to cause the failure; and,
* provides suggestions on the most effective period of time to perform preventive actions. Despite a thorough investigation of the denoted benefits, the predictive maintenance adoption by hospitals in Rwanda among other small- and medium-sized companies is still not mature due to differing reasons, either because of the historical data unavailability, trustless of the technology, or associated initial cost.