Internet of Things for enabling smart environments: A technology-centric perspective

2.1.Smart home

Fig. 1.

Main components in smart home systems.

Homes are environments particularly suitable to host smart technologies for three main reasons: (i) modern homes already contain a large number of technological devices, even if not always interconnected or interoperable; (ii) they are controlled environments and their owners can (at least in principle) provide the suitable investments for the deployment of technological solutions and provide to their maintenance, and (iii) they can provide to the inhabitants a large number of useful services and applications.

Services of smart homes vary widely. We broadly classify them as assistive services and management services (Fig. 1). Assistive services in smart homes aim at providing direct support to the users in their interests [77] and in their daily actions and activities that take place in the home [15]. For example, if watching television or listening to music are particular user interests, the smart home can assist by scheduling sources of noise caused by machine automation (such as the activation of the washing machine) at different hours, or by setting appropriately the lights and the multimedia devices configuration, according to the users’ preferences. Assistive services may also be tailored to some special needs of the user, especially when the user is an elder or a disabled or just sick, like ambient assisted living [68] or e-health [18] services.

Further, management services are those that address specific functionalities of the smart home. Examples of such services can be those concerning the security and safety of inhabitants (for example the anti-intrusion alarms or the detection of gas leaks) [35] or those concerning energy efficiency of the home [65], such as those that control ventilation and solar panels for the energy production and those that control the appliances and lights to reduce energy consumption while satisfying the inhabitants’ needs.

Although individually different, smart home services are usually developed at the application level of context-aware systems, which, in turn, are built on top of common mechanisms and functions. In fact, at the foundation of such systems are home automation mechanisms, which provide the ability to monitor/control the building blocks of a home like windows, doors, electrical system, air conditioning system, energy production subsystem, alarm, appliances and so forth.

Furthermore, most advanced services also need information about the inhabitants that can be obtained by a combination of environmental and wearable sensors, possibly those embedded in the users’ personal devices such as smartphones for example. Such additional sensors are necessary to obtain a detailed context about the users that is crucial to make the smart home an intelligent environment at the service of the user. Examples of such information are the user position (by means of a localization system), the user physical condition (by means of wearable sensors) or the user activities (by means of combined wearable and environmental sensors).

However, this information, which is often high-level, cannot be obtained by a direct observation of raw data coming from sensors. Instead, it must be processed by suitable data fusion algorithms, usually based on signal processing, machine learning and/or data analysis algorithms.

From the perspective of the information and communication technology (ICT), support for the full range of these mechanisms and services requires the development of a complete and complex IoT infrastructure. Such infrastructure must span the full range from the sensor/state data acquisition, storage and fusion to the data presentation and control of the actuation mechanisms present in the home, and should also include the appropriate support for deployment, commissioning and maintenance.

Employing contemporary standards and technologies, basic functionalities for sensing, state acquisition and control can already be achieved with relative ease. In particular, at the level of home automation, several standards [38] already provide adequate support, which include accessing state or sensor information and binding this information to the control/actuation mechanisms. Similarly, both ZigBee [32] and Bluetooth [30] support the integration of wearable devices and, at an upper layer, M2M standards [3] including but not limited to MQTT and CoAP, provide the mechanisms necessary to integrate the smart home in cloud services over the Internet. On the other hand, the development of services and applications in smart homes reflecting use cases and business plans of rich application scenarios is usually supported by specialized platforms for example, see Section 2.2.3 for AAL [28].

Fig. 2.

General description of IoT or smart environments for health.

Another aspect of IoT that has been confined so far to research, concerns the adaptation and personalization of services offered by smart homes. Adaptation and personalization consider the ability of a smart home to automatically tailor its services to the individual user’s needs. This is often achieved by building on generic services designed for a specific group of users such as children or the elderly, and then by adapting the behavior of the service to the habits of the user. This implies the ability to detect habits as well as to discover deviations from these habits while at the same time, to automatically reconfigure the business logic of the service or application. Such mechanisms usually require a strong convergence among sensors and activity recognition, anomaly detection and cognitive capabilities, especially in those cases in which at least part of these capabilities are integrated within the sensors themselves. Significant preliminary experiences with this approach which is often referred to as the Internet of Intelligent Things [4], has been obtained by EU projects RUBICON [56] and OPPORTUNITY [6].

2.2.Smart health

Over the last twenty years, due to significant reductions to the cost of sensors and improvements in both signal processing techniques and integration/quality of signal, applications of smart environments and Internet of Things to health have expanded rapidly. This Section relates some of the work in this domain with an overview description of enabling technologies.

2.2.4.Prevention/assistance to healthcare

The final goal of all the solutions of smart sensors and environments for health is to help prevent or assist the person that has to face a specific condition. Dependence is one of the huge cost in our healthcare systems and if it can be improved, it would reduce this part. A lot of work focuses on that considering different kinds of assistance. For people with a disability for instance, [44] proposes a system that will combine a web-app to allow the caregiver to give instruction to the environment and a smartphone to help the person in daily living. Such development has required expertise to design it as efficiently as possible so that it can be useful to the person. It generally includes co-conception with users.

One of the major concerns in health is monitoring and/or improving the condition of persons with chronic diseases or recurrent disorders. For instance, some smart devices and applications can help managing chronic diseases such as diabetes [47]. The goal of such applications, that relies generally on measurement devices and/or on smartphones and other connected devices is to help the person to manage the effect of the disease and control it or control the observance of medication. Solutions can also be integrated in objects daily used such as the bed [91]. In this application, the goal is to analyze the sleep conditions in order to detect and/or quantify changes in it and thus detect disorders. To this end, sensors and strain gauge are integrated to the bed. The long-term evolution of the uses of the person is important and relevant to the improvement or degradation of living conditions of the person. Other data can be acquired, such as heart rate, to be able to monitor in long terms the evolution of this data, and possibly in the future to raise alarms such as described before in the distress situations detection [78].

Fig. 3.

A subset of the most relevant domains in the smart city.

To conclude this Section, one of the important applications is the monitoring of and help offered to elderly people. In all developed countries, population is ageing fast due to improvements in medicine, and another fact is that the family is spreading all over countries and ageing people generally are lonelier than in the past. In addition to the works that monitor activities of daily living of the person, monitoring some characteristics of the living conditions of the person could be important. For instance, in [7] and [72], gait or time of transfers are measured along time, and their evolution is characterized. This evolution is another point of evaluating possible entrance in dependency. As a prevention, some works [12,31] propose to train the person with some specific exercises (link to posture, transfers or movements) to prevent fall or give benefit to the person.

Finally, in case of dependence, robots can be of help for the person to allow measurement and monitoring, and more importantly, to create a presence for the person and bring help in case of specific needs [70].

As seen in this Section, health applications are numerous and go from distress situation detection to long-term monitoring in smart environments. Such monitoring can be included in larger architectures. In the future, we could think that it can be included in smart city architectures. Smart cities are the subject of the next Section.

2.3.Smart city

Smart cities are one of the richest and most com-plex scenarios for smart environment (if not already the most complex) [86,87]. It crosses several domains including the environment, economy, mobility, energy, planning, governance among others (see Fig. 3) presenting a large number of associated challenges and involving multiple actors such as city administrators, operators, service providers and citizens, with possibly competing objectives.

It is thus clear that smart cities are not just a technological challenge, but they are the place in which the challenges to the technology are the most diverse and heterogeneous, and the technological solutions must confront themselves against many interests and needs.

Fig. 4.

The 4 industrial revolutions leading to the smart factory of the future and cyber-physical production systems.

From an ICT perspective, its technologies are of-ten transversal to all domains and challenges, and they address a number of scenarios of smart cities spanning from e-tourism [88], e-culture [11], e-government [61], smart energy [63], smart mobility [96], e-health/wellbeing [25], just to mention a few.

The heterogeneity of technical problems and of available technologies [84], united to political issues [54], are barriers that may delay the development of smart cities. It is thus no surprise that the interoperability of technological solutions and standards are of utmost importance, especially in the field of the Internet of Things, which is widely recognized as a key technological enabler of smart cities [95]. Most recent trends, under this respect, are the introduction of novel participatory sensing paradigms that involve the citizens themselves in the (expensive) task of sensing data from the cities, by means mobile applications for personal smartphones [17].

In addition to reducing or indeed avoiding the costs due to the deployment and maintenance of capillary sensory apparatus in the city, such paradigms have also the great advantage of empowering users in the development of smart cities. However, the relative youth of these approaches makes them even farther from a standardization, and many different experimental platforms have been tested in several smart cities [16,43,57]. Furthermore, recent approaches to participatory sensing are also experiencing novel forms of network organization based on emerging edge computing [10].

2.4.Smart factory and industry 4.0

Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments [74]. While acquiring, processing and acting upon various kinds of relevant context information is common in application areas such as smart homes and offices, smart automated manufacturing systems can benefit from these capabilities as well. For example, smart manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime or adapt to a customer’s personal preferences without any delay on the production process. The proliferation of smart enabling technologies has sparked a digital transformation in the manufacturing world. This paradigm shift is often referred to as the 4th Generation Industrial Revolution (Industry 4.0) [34,58] or the Factory of the Future (FoF) [50], as depicted in Fig. 4.

It envisions smart factories where the Internet of Things (IoT) and Cyber-Physical Systems (CPS)-enabled manufacturing [66] provide the foundations for creating smart products through smart processes and procedures. Large factories connect hundreds – if not thousands – of sensors and devices, not only within the plant, but also with other factories and the outside world. Smart products will plan, control and optimize their own production process with minimal human intervention by harnessing ongoing developments in sensor technology, machine-to-machine communication [90], big data analytics [69] and machine learning [71,75]. The purpose of this digital transformation is to enhance the transparency of the production process across the organizational boundaries of the manufacturing enterprise.

Enhanced access to data from the Industrial IoT (IIoT) [34] will support business applications on any device, any time, from any location. In turn, the data-intensive nature of smart production systems will enable timely, accurate and detailed log trails resulting in a real-time augmented view on many systems and activities in a way that was not previously possible. A consequence is that the production floor has become an inherently complex intelligent environment, as the digital and physical worlds are heavily intertwined.

Indeed, interconnected systems will be linked to cloud services for remote monitoring and data analytics to optimize production plans, enable proactive maintenance, and respond quicker to continuously changing customer requirements. The factory of the future will leverage data-accessing and data-processing services available on the Internet to support data-intensive business processes and time-critical applications, as depicted in Fig. 5.

Fig. 5.

Networked production and manufacturing.

With networked production as a key feature of Industry 4.0, people, machines and business processes will interact with one another to enable personalized products through flexible, resource-efficient and cost-effective manufacturing.

3.1.RFID technologies

RFID is the umbrella term in common use which covers a variety of distinct technologies using a wide range of frequencies, communication protocols, and device types. Moreover, RFID technologies have been standardized under many different organizations including international such as ISO, ITU and IEC as well as national such as DIN (Germany), JIS (Japan) and SINIAV (Brazil). The confusing landscape of RFID is further complicated by the common use of commercial brand names such as Mifare and RAIN RFID as alternatives to the difficult to use alphanumeric standard names.

To provide some structure and help navigate the range of RFID technologies a common approach is to refer to different technologies using the names of the corresponding frequency band as summarized in Table 1. Furthermore, often RFID technologies are characterized with reference to their tag chip technology, which also defines their communication characteristics, as summarized in Table 2.

Despite the great variety of RFID technologies and flavors on offer, the vast majority of applications developed in the context of the IoT reviewed in Section 2 above typically employ one of the three most common types summarized in Table 3. This table provides summary information that can be used to select the particular flavors that meet the requirements of a particular IoT application matching specific characteristics such as the number of co-located tags that must be supported (depending on the ability of the technology in terms of reading speed and its ability to avoid collisions), security provisions, tag capacity, range, tag packaging and form as well as the ability to easily integrate readers in bespoke system designs.

3.2.Identifier schemes

As noted earlier, for the development of open IoT systems, which typically incorporate a variety of stakeholders and must support scalable operation, RFID must support a common way to interpret identifiers retrieved from tags [81]. Moreover, standard identifier schemes are already in widespread use for a variety of material objects, locations and even digital artifacts so demanding that RFID within the IoT starts from a clean slate would not be feasible either from a financial or an organizational point of view. Electronic Product Codes (EPC) developed by SG-1 and EPCglobal, Object Identifiers (OID) according to the ISO/ITU standard, Ubiquitous IDs (uID) in wide use in Japan as well as a variety of other schemes commonly employed in RFID and barcode encodings, often in an industry-specific manner, are in current common use for the identification of billions of already tagged objects.

Although it is not possible to cover each one of these schemes in detail in this paper, we note that all schemes follow a similar pattern to structure their codes. Specifically, each code starts with a prefix which identifies the particular type of code that follows, for example within the EPC system, the prefix of 00110000 identifies the remainder of the codes as a Serialized Global Trade Item Number of length 96 bits (SGTIN-96). The remainder of the code is then typically organized in a hierarchical manner to enable code allocation delegation across regions or organizations. In the case of SGTIN-96 codes for example, the code will include the company prefix identifying the manufacturer of the particular product, the item reference identifying the product type and finally a serial number that is unique to the particular product item. Similar structures exist for OID (specified under Section 9834-8:2009 of ISO/IEC) and uID although of course identifier space management and regulation are carried out through their respective organizational custodians.

3.3.Identifier resolution systems

Notwithstanding the specific choice of identifier use, any IoT system that incorporates RFID-tagged entities must provide an automated way to discover information associated with a particular identifier as well as control access to this information in a manner that ensures secure operation and privacy protection. In the content of IoT RFID, this system capability is referred to as the Identifier Resolution Service(s).

Many of the current proposals for IRSs provide specifications for scheme-specific services: for example, EPCglobal [55] provides resolution services that cater only for EPC codes issued within their ecosystems. Ongoing work by ISO/IEC JTC 1/SC 6/WG 9 and ITU-T Q12/SG17 has developed an OID resolver specification known as X.oid-res or the SG17 ORS (OID Resolution Service). In contrast to that work, IoT applications require an open and inclusive approach. For example, the extensive experience and experimentation in this area conducted within the Internet Engineering Task Force (IETF) and specifically the work carried out on the HIP (RFC 5201) and LISP (RFC 6115) protocols and on scalable Internet-scale resolution systems [39] offers valuable lessons on how to enable IoT-scale IRS that can cater to all types of RFID irrespective of the specific UUID scheme employed. At the time of writing, there is no commonly accepted standard but certain developments point the way towards a usable solution [80].

4.1.IEEE 802.15.4

IEEE 802.15.4 is a family of wireless technologies intended to enable monitoring and control applications for Wireless Personal Area Network (WPAN). Publication of its first version in 2003 was a milestone, since for the first time, an open standard targeted low-rate communication, with a focus on simplicity and low energy consumption [9].

IEEE 802.15.4 was not designed for a specific application domain. Instead, it is intended as a generic technology, and it has become the basis of relevant protocol architectures, supporting IPv6, and also non-IP-based protocol solutions such as ZigBee (see Section 5). Nevertheless, IEEE 802.15.4 has been optimized for specific environments, such as the IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) mode, designed to overcome impairments in industrial environments [93]. In fact, TSCH is also used in standard protocol stacks on top of IEEE 802.15.4 for industrial environments such as ISA 100.11a and WirelessHART.

4.2.BLE

BLE was released in 2010 as a low-energy variant of classic Bluetooth [37]. Since BLE can partially reuse Bluetooth hardware, a device that supports classic Bluetooth can also support BLE for low additional cost. Therefore, BLE can leverage its widespread presence in smartphones, which can be used to collect data from or send commands to surrounding sensors and actuators. The smartphone can also be used as a gateway for interaction between sensors, actuators, and the Internet. In addition, BLE has become dominant in the areas of wearables, tablets, and other consumer electronics devices [1].

4.3.ITU-T G.9959 (Z-Wave)

ITU-T G.9959 is an open standard that specifies the lower layers of the Z-Wave technology. Z-Wave is a wireless protocol stack that was born as a proprietary technology, which has been specifically designed for home automation [38].

4.4.DECT-ULE

DECT-ULE is a low-energy variant of DECT, which is the main technology used for voice and data communication for indoor cordless telephony [93]. Use of DECT-ULE has been proposed in order to enable communication between a gateway and sensors or actuators in the home, by exploiting the strong presence of DECT equipment.

4.5.NFC

NFC is a wireless technology that provides very short range (e.g. ∼10 cm). This feature offers intrinsic security properties, since it minimizes opportunities for unauthorized devices to capture transmitted data. NFC allows different communication modes, such as card emulation (e.g. for payment applications), reader mode, and peer-to-peer communication [41].

4.6.IEEE 802.11

IEEE 802.11 is a massively successful Wireless Local Area Network (WLAN) family of standards, often referred to as Wi-Fi. While its design includes power-saving mechanisms, it has not been a clear choice for energy-constrained devices, given its implementation complexity and overall power consumption. In order to fill the gap of IEEE 802.11 for sensor/actuator applications, IEEE 802.11ah has been recently designed for low energy consumption, lower bit rate and increased range, enabling sensor data collection application areas such as smart grid [8].

4.7.LoRaWAN

LoRaWAN is an unlicensed band wireless technology that belongs in the emerging Low Power Wide Area Network (LPWAN) category. LoRaWAN uses LoRa technology at the physical layer, and it allows an increased communication range up to the order of 10 s of kms. Based on a star topology, whereby a gateway collects data from up to hundreds of thousands of devices such as sensors, it offers a low infrastructure cost, at the expense of severe message rate and bit rate limitations [29].

4.8.Sigfox

Sigfox is another flagship LPWAN wireless technology, therefore it offers long range and low infrastructure coverage for a massive amount of devices, at the expense of very reduced bit and message rates. This technology operates in unlicensed frequency bands, and it is managed by the company that is also called Sigfox. Like other LPWAN technologies, it is based on the star topology, and a communication range in the order of 10 s of kms is achievable [29].

4.9.NB-IoT

Narrowband IoT (NB-IoT) is another emerging technology that is often considered to be in the LPWAN category [92]. NB-IoT has been defined in the Release 13 specification by 3GPP, it is based on licensed spectrum, and it also offers support of a large number of devices per single base station, at low bit rates.

4.10.PLC

Power Line Communication (PLC) defines a family of technologies that leverage power grid infrastructure as a means for communication. While PLC is based on using a wired medium, it is subject to interference, therefore it is subject to impairments similar to those of wireless media. There exist low bit rate PLC variants such as IEEE 1901.2 or ITU-T G.9903, which are often used in smart home or related applications such as smart grid [42].

4.11.MS/TP

Master Slave/Token Passing (MS/TP) is a wired technology that belongs to the BACnet family of standards for building automation. Devices that use MS/TP are typically grid-powered. While the described features do not pose the same degree of limitations as other technologies overviewed in this Section, devices that use MS/TP are constrained, and the physical layer, based on RS-485 specification, offers low bit rates [62].

5.1.IP-based architectures

In order to obtain the maximum benefit from sensors and actuators, they need to be connected to the Internet, which allows the highest degree of flexibility (i.e. making it possible to communicate with these devices remotely) and interoperability (i.e. Internet connectivity maximizes the number of devices that one device can talk to).

Running IP can be considered the most suitable method for many constrained devices to achieve Internet connectivity. IP is an open protocol, it was designed for interoperability, and it simplifies application development, since applications run on well-known services on top of IP, regardless of the underlying technology [39].

The least constrained devices (e.g. class 2 or greater [14]) are considered to be able to run a traditional IP-based protocol stack (e.g. using HTTP, TCP, and IP, on top of common network interfaces, see Fig. 6-left, especially when such devices are grid-powered.

Fig. 6.

IP-based protocol architectures, including typical application layer and transport layer protocols: Classic architecture (left) and IoT-specific architecture (right). RPL is only used for multihop topology IoT networks.

However, the most constrained devices, and/or those that are energy-constrained, cannot afford the more traditional protocol stack, due to lack of sufficient computational power and energy availability. For this reason, the IETF engaged in 2005 in a decade-long process to define adaptations, optimizations and new protocols for a first version of an IPv6-based lightweight protocol stack for constrained devices. IPv6 was assumed at the core of the stack, since IPv4 faces the problem of address exhaustion, and also because IPv6 is provided with tools for device self-configuration. The lightweight, IoT-specific protocol stack (see Fig. 6-right) comprises an adaptation layer below IPv6 [82], the IPv6 Routing Protocol for Low-power and lossy networks (RPL) [94], and the Constrained Application Protocol (CoAP) [83].

IPv6 over Low power WPAN (6LoWPAN) is an adaptation layer that was designed in order to support IPv6 over IEEE 802.15.4 networks. Among others, 6LoWPAN provides IPv6 (and UDP) header compression, packet fragmentation and reassembly (given the short IEEE 802.15.4 frame size), and an adaptation of the IPv6 Neighbor Discovery protocol for energy-constrained devices [82].

The 6LoWPAN work has been extended to several other technologies. For example, as of the writing, the IETF effort called 6Lo has adapted 6LoWPAN for interfaces such as BLE, DECT-ULE, ITU-T G.9959, MS/TP, and NFC [1]. On the other hand, the effort called IPv6 over Time Slotted Channel Hopping (6TiSCH) has enabled IPv6 support over IEEE 802.15.4e TSCH mode [93]. Finally, the IETF has recently targeted IPv6 support over LPWAN technologies, such as Sigfox, LoRaWAN and NB-IoT [64]. While this effort also provides header compression and fragmentation, the extremely severe communication constraints in some of the considered technologies, requires this adaptation layer to go significantly beyond 6LoWPAN-style adaptation.

At the network layer, for networks that follow a multihop topology (e.g. the mesh topology), a routing protocol is needed. RPL was designed for sensor data collection applications. In fact, RPL is optimized when all nodes in a network need to report data to a single destination, by minimizing routing table memory requirements and message overhead. However, RPL is not optimal for any-to-any operation and requires additional mechanisms in such case [22].

Finally, at the application layer, CoAP was designed to follow the Representational State Transfer (REST) principles, which are also used in the World Wide Web (WWW) [13]. CoAP can be viewed as a lightweight cousin of HTTP, as it allows the manipulation of resources identified by Uniform Resource Identifiers (URIs), and messages are self-contained, albeit with a lightweight binary header, and based (by default) on UDP.

5.2.Non-IP-based architectures

There exist also complete protocol architectures for sensor and/or actuator devices that are not based on IP. For example, ZigBee defined its own upper layer functionality on top of IEEE 802.15.4. BLE and Z-Wave are other non-IP-based complete protocol stacks. Internet connectivity for non-IP-based protocol stacks can be achieved by means of Protocol Translation Gateways (PTGs), which pose management issues and limit application development. Nevertheless, ZigBee, BLE and Z-Wave also support protocol stack variants that are based on IP.

6.1.IoT solutions features and smart environments

The following features of IoT solutions used in the selected smart environments are discussed first, specifically the type of sensors used, CNN topology, technology capabilities, and computing approach (see Table 5).

6.2.Challenges

The following challenges for IoT solutions in smart environments are considered: interoperability and standardization, adaptation and personalization, and entity identification and virtualization.