# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Modelling of Services  
Modelling of services and their interaction is key to the successful deployment of IoT devices, since, in contrast with the past, multifarious large industry software systems or applications are built from modelling services. Emerging technologies however, including IoT and CPS, pose more challenges since they need to be seamlessly integrated into already established existing models.  
The backbone of IoT is based on the Internet infrastructure and the offer of real-world services. With advances in the Internet through such technologies as the SDN this also helps spread the IoT. The key to the success of the wide-scale adoption of IoT is the ability to provision real-world services. This will entail seamlessly communicating with heterogeneous objects. The sensed data from the physical world needs to be filtered and matched to precisely defined applications. Data fusion needs to be carried out from the collected disparate IoT sensors and the information presented in a meaningful manner. The decision making may be augmented by artificial and cognitive intelligence supporting autonomous reasoning. Further research needs to concentrate on the middleware to support all these new approaches and algorithms and seamless integration with the application layer.  
The \"Time to Live\" (TTL) parameter from Internetworking is carried over to IoT data, combined with semantic modelling, annotation and metadata. Negotiations between the various IoT devices need to be carried out as fast as possible to reduce network delay. These device negotiations are necessary to discover what capability and services they can offer. Thus, IoT network discovery needs to reach a stable state as fast as possible despite the number of devices. Semantic modelling will need to take into account the unique constraints of IoT devices, namely their limited electrical power, memory and computational ability (noting, however, that these are expected to evolve in accordance with Moore's Law). The semantic modelling of IoT networks is carried out on powerful machines, such as the gateway nodes and in the middleware. Thus, the computational burden is shifted away from the IoT device, so that they can concentrate on gathering their sensed data. This concept of shifting the computational burden of those intensive tasks is similar to the approach adopted by the mobile communications networks. This also allows IoT devices to be queried more efficiently by software agents. Differing modes of processing and communication links will need to be utilized depending on where the data is in the network. Around the IoT device, low power and low bandwidth links will be required. Data gathered by data fusion can be processed in the middleware. Automation of all processes is essential, including making the IoT device as autonomous as possible. This is particularly important when it comes to the manual annotation of IoT devices used in semantic modelling: for a few devices in a controlled environment, manual annotation is possible, but as the number of devices increases this will clearly become impossible for a human operator.