# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### The Mathematical Tools and Techniques of OR Which May Support the IoT
#### 3.1.1 Data Analytics/Databases  
Data Analytics is the science of studying data to draw conclusions. With billions or even trillions of devices connected to the IoT, there will be vast amounts of data including identification, positional, environmental, historical, and descriptive data [45]. This IoT data will create data management and analysis issues. According to [PERSON] et al. [46], IoT data will have characteristics of heterogeneity, inaccuracy, massive real time, and implicit semantics leading to significant data management issues.  
[PERSON] and [PERSON] [45] and [PERSON] et al. [46] addressed challenges for database management in IoT that will bring vast amounts of data. Data is categorized into RFID, address/identifiers, description, positional and environmental, sensor, historical, physics and command. eXtensible Markup Language (XML) offers a means of representing unstructured data; while Structured Query Language (SQL) is unlikely to be useful since IoT data will not be uniform and structured. Service Oriented Architecture may be used to support interoperability among IoT systems. Data indexing, archival and protection must also be considered with respect to national data protection laws. Methods for querying semi-structured data, data streaming, sampling continuous data and data mining will need to be developed to manage the size of IoT databases.  
1.2 Decision Analysis/Support Systems Including Analytic Hierarchy Process (AHP), Multi-Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA)  
[PERSON] et al. [15] developed a business model framework for the IoT using a literature survey and interviews. Their model has nine building blocks: customer streams, value proposition, channels, customer relations, revenue streams, key resources, key activities, key partners and cost structure. The model showed that the value proposition is the most important item while customer relationships and key partnerships are also considered important. Data were sourced mainly from Netherlands and US as the authors acknowledge.  
[PERSON] et al. [19] explored the challenges relating to the development IoT business models. These include the diversity of the objects, the general immaturity of IoT and the unstructured nature of the IoT ecosystems. They suggest a potential solution with a conceptual model that includes value drivers, value nodes, value exchanges, and value extracts as pillars.  
[PERSON] and [PERSON] [16] adapted an Analytic Hierarchy Process model to three IoT applications: healthcare, logistics, and energy management using criteria of technology, market potential, and regulatory environment. Survey data that were analyzed using the model showed that market potential was the most important criterion in the first layer of the model and concluded that IoT logistics is the most promising application from the perspective of ICT experts. Healthcare needs to overcome user barriers and technical reliability to be accepted whereas energy management requires government support (Korean Smart Grid initiative [11]).  
[PERSON] et al. [47] combined multiple criteria decision making with a soft systems approach for what they term'messy problems', ill-structured situations with multiple independent problems. This approach is applied to several test cases including a rural telecommunication system in South Africa. This approach may also be applicable to complex ICT systems that will make up the IoT.  
Decision support has been widely used for IoT applications, generally for assessment of potential IoT solutions. It is likely to continue to be an important tool as the range of IoT applications develops.