(being continued from 29/07/16)
3.3. IoT elements
We present a taxonomy that will aid in defining the components required for the Internet of Things from a high level perspective.
Specific taxonomies of each component can be found elsewhere [12–14]. There are three IoT components which enables seamless ubicomp: (a) Hardware—made up of sensors, actuators and embedded communication hardware (b) Middleware—on demand storage and computing tools for data analytics and (c) Presentation—novel easy to understand visualization and interpretation tools which can be widely accessed on different platforms
and which can be designed for different applications. In this section,we discuss a few enabling technologies in these categories which will make up the three components stated above.
3.3.1. Radio Frequency Identification (RFID)
RFID technology is a major breakthrough in the embedded communication paradigm which enables design of microchips for wireless data communication. They help in the automatic identification of anything they are attached to acting as an electronic barcode
[15,16]. The passive RFID tags are not battery powered and they use the power of the reader’s interrogation signal to communicate the ID to the RFID reader. This has resulted in many applications particularly in retail and supply chain management. The applications
can be found in transportation (replacement of tickets, registration stickers) and access control applications as well. The passive tags are currently being used in many bank cards and road toll tags which are among the first global deployments. Active RFID readers
have their own battery supply and can instantiate the communication.
Of the several applications, the main application of active RFID tags is in port containers  for monitoring cargo.
3.3.2. Wireless Sensor Networks (WSN)
Recent technological advances in low power integrated circuits and wireless communications have made available efficient, low cost, low power miniature devices for use in remote sensing applications.
The combination of these factors has improved the viability of utilizing a sensor network consisting of a large number of intelligent sensors, enabling the collection, processing, analysis and dissemination of valuable information, gathered in a variety of environments . Active RFID is nearly the same as the lower endWSNnodes with limited processing capability and storage. The scientific challenges that must be overcome in order to realize the enormous potential of WSNs are substantial and multidisciplinary in nature . Sensor data are shared among sensor nodes and sent to a distributed or centralized system for analytics. The components that make up the WSN monitoring network include:
(a) WSN hardware—Typically a node (WSN core hardware) contains sensor interfaces, processing units, transceiver units and power supply. Almost always, they comprise of multiple A/D converters for sensor interfacing and more modern sensor nodes have the ability to communicate using one frequency band making them more versatile .
(b) WSN communication stack—The nodes are expected to be deployed in an ad-hoc manner for most applications. Designing an appropriate topology, routing and MAC layer is critical for the scalability and longevity of the deployed network. Nodes in a WSN need to communicate among themselves to transmit data in single or multi-hop to a base station. Node drop outs, and consequent degraded network lifetimes, are frequent. The communication stack at the sink node should be able to interact with the outside world through the Internet to act as a gateway to the WSN subnet and the Internet .
(c) WSN Middleware—A mechanism to combine cyber infrastructure with a Service Oriented Architecture (SOA) and sensor networks to provide access to heterogeneous sensor resources in a deployment independent manner . This is based on the
idea of isolating resources that can be used by several applications.
A platform-independent middleware for developing sensor applications is required, such as an Open Sensor Web Architecture (OSWA) . OSWA is built upon a uniform set of
operations and standard data representations as defined in the SensorWebEnablement Method (SWE) by the Open Geospatial Consortium (OGC).
(d) Secure Data aggregation—An efficient and secure data aggregation method is required for extending the lifetime of the network as well as ensuring reliable data collected from sensors . Node failures are a common characteristic of WSNs,the network topology should have the capability to heal itself.
Ensuring security is critical as the system is automatically linked to actuators and protecting the systems from intruders becomes very important.
3.3.3. Addressing schemes
The ability to uniquely identify ‘Things’ is critical for the success of IoT. This will not only allow us to uniquely identify billions of devices but also to control remote devices through the Internet.
The few most critical features of creating a unique address are:
uniqueness, reliability, persistence and scalability.
Every element that is already connected and those that are going to be connected, must be identified by their unique identification,location and functionalities. The current IPv4 may support to an extent where a group of cohabiting sensor devices can be identified geographically, but not individually. The Internet Mobility attributes in the IPV6 may alleviate some of the device identification problems; however, the heterogeneous nature of wireless nodes,variable data types, concurrent operations and confluence of data
from devices exacerbates the problem further .
Persistent network functioning to channel the data traffic ubiquitously and relentlessly is another aspect of IoT. Although,the TCP/IP takes care of this mechanism by routing in a more reliable and efficient way, from source to destination, the IoT faces a bottleneck at the interface between the gateway and wireless sensor devices. Furthermore, the scalability of the device address of the existing network must be sustainable. The addition of networks and devices must not hamper the performance of the network,
the functioning of the devices, the reliability of the data over the network or the effective use of the devices from the user interface.
To address these issues, the Uniform Resource Name (URN) system is considered fundamental for the development of IoT. URN creates replicas of the resources that can be accessed through the URL. With large amounts of spatial data being gathered, it is often
quite important to take advantage of the benefits of metadata for transferring the information from a database to the user via the Internet . IPv6 also gives a very good option to access the resources uniquely and remotely. Another critical development in
addressing is the development of a lightweight IPv6 that will enable addressing home appliances uniquely.
Wireless sensor networks (considering them as building blocks of IoT), which run on a different stack compared to the Internet,cannot possess IPv6 stack to address individually and hence a subnet with a gateway having a URN will be required. With this in mind, we then need a layer for addressing sensor devices by the relevant gateway. At the subnet level, the URN for the sensor devices could be the unique IDs rather than human-friendly names as in the www, and a lookup table at the gateway to address this device. Further, at the node level each sensor will have a URN (as numbers) for sensors to be addressed by the gateway. The entire network now forms a web of connectivity from users (high-level)
to sensors (low-level) that is addressable (through URN), accessible (through URL) and controllable (through URC).
3.3.4. Data storage and analytics
One of the most important outcomes of this emerging field is the creation of an unprecedented amount of data. Storage, ownership and expiry of the data become critical issues. The internet consumes up to 5% of the total energy generated today and with these
types of demands, it is sure to go up even further. Hence, data centers that run on harvested energy and are centralized will ensure energy efficiency as well as reliability. The data have to be stored and used intelligently for smart monitoring and actuation. It is important to develop artificial intelligence algorithms which could be centralized or distributed based on the need. Novel fusion algorithms need to be developed to make sense of the data collected.
State-of-the-art non-linear, temporal machine learning methods based on evolutionary algorithms, genetic algorithms, neural networks,and other artificial intelligence techniques are necessary to achieve automated decision making. These systems show characteristics
such as interoperability, integration and adaptive communications.
They also have a modular architecture both in terms of hardware system design as well as software development and are usually very well-suited for IoT applications. More importantly, a centralized infrastructure to support storage and analytics is required.
This forms the IoT middleware layer and there are numerous challenges involved which are discussed in future sections. As of 2012, Cloud based storage solutions are becoming increasingly popular and in the years ahead, Cloud based analytics and visualization
platforms are foreseen.
Visualization is critical for an IoT application as this allows the interaction of the userwith the environment. With recent advances in touch screen technologies, use of smart tablets and phones has become very intuitive. For a lay person to fully benefit from the IoT
revolution, attractive and easy to understand visualization has to be created. As we move from 2D to 3D screens, more information can be provided in meaningful ways for consumers. This will also enable policy makers to convert data into knowledge, which is critical in fast decision making. Extraction of meaningful information from raw data is non-trivial. This encompasses both event detection and visualization of the associated raw and modeled data, with information represented according to the needs of the end-user.
There are several application domains which will be impacted by the emerging Internet of Things. The applications can be classified based on the type of network availability, coverage, scale, heterogeneity,repeatability, user involvement and impact . We categorize the applications into four application domains: (1) Personal and Home; (2) Enterprize; (3) Utilities; and (4) Mobile. This is depicted in Fig. 1, which represents Personal and Home IoT at the scale of an individual or home, Enterprize IoT at the scale of
a community, Utility IoT at a national or regional scale and Mobile IoT which is usually spread across other domains mainly due to the nature of connectivity and scale. There is a huge crossover in applications and the use of data between domains. For instance,
the Personal and Home IoT produces electricity usage data in the house and makes it available to the electricity (utility) company which can in turn optimize the supply and demand in the Utility IoT. The internet enables sharing of data between different service
providers in a seamless manner creating multiple business opportunities.
A few typical applications in each domain are given.
(TO BE CONTINUED)