## 4 Findings  
This section illustrates different works that addressed integrating blockchain technology and IoT to tackle various privacy and security aspects. We discuss the different characteristics of all included papers, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and finally further notes for consideration. We note that the calculations in our discussion depend only on our findings of the included papers. The primary information of the included papers [90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128] is shown in Table 1. Based on the data shown in Figure 4, we can observe that 13.95% of the total papers were published in 2018 [93; 103; 111; 116; 120; 124], while 30.23% were published in 2019 [90; 92; 94; 95; 99; 101; 102; 104; 114; 117; 119; 122; 127], 11.64% were published in 2020 [91; 96; 113; 118; 121], 13.95% were published in 2021 [100; 112; 115; 123; 125; 128], and finally, 30.23% were published in 2022 [97; 98; 105; 106; 129; 107; 129; 120; 129; 130; 131; 132]. We note that blockchain technology research is considered new as it will require further development and testing in a real-time environment. In addition, the implementation of such systems is not easy. It requires more time than traditional architectures, which explains why blockchain-based approaches are not widely used in some research areas. However, many approaches that include blockchain technology in their designs are achieving promising results. Overall, 37.21% of the proposed schemes originate from China [92; 94; 95; 96; 97; 102; 104; 105; 113; 114; 119; 124; 125; 128; 129; 132], and 62.79% are distributed between 14 countries [90; 91; 93; 98; 99; 100; 101; 103; 106; 107; 108; 112; 115; 118; 120; 123; 126; 127; 130; 131]. Most of the included papers are published in journals, which result in 79.07% [90; 91; 94; 96; 97; 98; 99; 100; 102; 104; 105; 118; 119; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131], while 16.28% are published in conferences [92; 93; 95; 101; 103; 116; 120], and 4.65% are published in book and symposium [117; 124]. We included a detailed description of the publishers (name of journal or conference) to address the common databases used. Overall, 51.16% are published in the IEEE database [91; 92; 96; 97; 100; 101; 102; 104; 108; 1109; 111; 116; 119; 120; 123; 124; 127; 128; 132], 20.93% are published in Elsevier [94; 103; 112; 113; 114; 117; 118; 121; 122], 13.95% are published in  
Figure 3: Study selection criteria. The objective and development level are presented in Table 2. Based on the data shown in Figure 5, our results show that 27.9% of the included studies focused on the healthcare domain [99; 100; 101], while 18.6% focused on proposing generic approaches [91; 92; 93; 94; 95; 96; 97; 98]. From the total percentage of the included articles, 23.25% aimed to target smart environments applications divided into 9.3% for smart home applications [120; 121; 122; 123], 6.98% presented systems designed to target smart cities [116; 117; 118], and there were 2.32% of each of the following: smart factory [119], smart traveling [124], and smart agriculture [130]. Furthermore, the IoT device gateway [111; 112], IoT information systems [113; 114], management systems [131; 132], cloud environment [128; 129], and fog computing [125; 126] have carried out 4.65% for each separate application. Finally, the rest (\(\approx\)6.99%) aimed to address edge computing [90], mobile IoT applications [115], and reputation systems [127]. Regarding the development level, we used Technology Readiness Level (TRL) as the base concept to describe the development level of the included articles. TRL includes nine