- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Journal of Sensors
Volume 2013 (2013), Article ID 491092, 12 pages
Radio Context Awareness and Applications
1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
2Departement de Telecommunications, SUPELEC, 91192 Paris, France
3Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey
4Dipartimento di Ingegneria dell'Informazione, Università di Firenze, 50139 Firenze, Italy
5Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Received 19 April 2013; Accepted 12 August 2013
Academic Editor: Andrea Cusano
Copyright © 2013 Luca Reggiani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The context refers to “any information that can be used to characterize the situation of an entity, where an entity can be a person, place, or physical object.” Radio context awareness is defined as the ability of detecting and estimating a system state or parameter, either globally or concerning one of its components, in a radio system for enhancing performance at the physical, network, or application layers. In this paper, we review the fundamentals of context awareness and the recent advances in the main radio techniques that increase the context awareness and smartness, posing challenges and renewed opportunities to added-value applications in the context of the next generation of wireless networks.
Modern wireless systems are required to manage radio resources in an effective and flexible manner in order to maximize the network capacity. Moreover, they are increasing their complexity and pervasiveness in the real world at several scales, giving rise to heterogeneous, more decentralized, and sophisticated systems. In this context, next generation radio systems are required to achieve high capacity, high reliability, flexibility, adaptivity, and full support to innovative applications and services that make use of the awareness of both the context and the surrounding environment. Therefore, collaboration, awareness, smartness, and adaptivity are the keywords of a trend that involves all the radio technologies, from wireless personal area networks (WPAN) to wireless metropolitan area networks/wide area networks (WMAN/WAN) and wireless regional area networks (WRAN) as depicted in Figure 1. Wireless sensor networks (WSN), whose radio technologies belong mainly to WPAN, play a fundamental role in the field of radio context awareness: these sensing networks are usually composed of a large number of low-power, inexpensive, and long-life nodes with sensing, computation, and wireless communication capabilities, and they can produce, with several degrees of performance and complexity, the features and the related applications that will be discussed in this paper.
As stated efficaciously in , context regards “any information that can be used to characterize the situation of an entity, where an entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.” Therefore, radio context awareness concerns the ability of detecting a system state or estimating a system parameter in order to enhance communication performance from the physical to the network layers or in order to provide the necessary context information for advanced services and applications. Channel state information, energy consumption, positioning and environment mapping, mutual interference among active devices, and spectrum usage are examples of system state information that can enhance either the efficiency of the communication network itself or the effectiveness of the applications which can be built on this context knowledge [2–12]. Nowadays, it is clear that radio context aware techniques can give a further strong impulse to new applications and enhance the communication performance operating on several aspects of the system.
This trend is enforced also by the ongoing Internet of Things (IoT) development, in which a pervasive connection of the environment will facilitate sensing, communication, and interaction capabilities which have never been observed until now. IoT and Internet will provide a potentially dramatic increase of earth awareness, and in the future communication platforms, characterized by relevant and increasing levels of spread awareness, the radio technologies will play a crucial and irreplaceable role either for their mobile and flexible communication capability or for the information that can be extracted from the surrounding environment [13, 14]. Thanks to the properties of electromagnetic propagation, a radio transceiver is not only a communication device anymore but a sensor itself of the surrounding context and environment. Therefore, wireless communications are one of the key enabling technologies of this new paradigm. Examples of potential application fields cut across all the most promising next generation systems and infrastructures, as (i) eHealth (electronic health systems), (ii) smart vehicles and intelligent transportation infrastructures, (iii) smart buildings and homes, (iv) smart energy grids, (v) environment monitoring and control, and (vi) context aware mobile services.
In this review paper, we particularly take an interest in those techniques which, via radio transmission, provide information that can enhance context aware applications. Section 2 describes the main concepts and the radio context awareness model we use for categorizing the contributions in this field. In particular we separate the contributions of radio awareness that are used to enhance system performance from those that can be used in the application layer for specific services and functions. The next sections are dedicated to the three main sectors that have been active in the recent years in this area and that can provide a further contribution and development in the near future: Section 3 presents a survey of identification of persons or objects through wireless technologies, Sections 4 and 5 are devoted to the vast areas of radio detection and radio localization respectively. All these sections are the expression of the main contributions to context awareness that arise from the radio physical layer of modern and future communication systems.
2. The Radio Context Awareness Model
Awareness is the prerequisite for any cognitive activity and therefore to what we commonly call intelligence. In , Mitola classified the cognition tasks in terms of nine levels of capability in a radio network; the process from a standard to an intelligent radio includes the following fundamental steps, characterized by an increasing level of cognition ability. (i)Context or environment awareness with minimum user involvement. (ii)Capability of learning and planning according to an objective. (iii)Capability of adapting autonomously, modifying plans and protocols.
Since Mitola’s work [15, 16], the concept of cognitive radio has opened a wide research activity, with the objective of improving spectrum efficiency and design flexibility of radio systems. In particular, the spectrum usage in terms of opportunistic and efficient exploitation of available, free radio channels has been the main research subject in this field, and spectrum sensing has become the fundamental, enabling function for the physical layer of cognitive radio systems [2, 3]. Many times it has been observed how the evolution of the congestion of the licensed spectrum has not avoided its underexploitation, leading to the necessity of new spectrum sharing systems . Haykin, in , formulated the following definition of cognitive radio in a radio network: “cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world) and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming radio frequency (RF) stimuli by making corresponding changes in certain operating parameters (e.g., transmit power, carrier frequency, modulation, and coding schemes) in real time, with two primary objectives in mind: (i) highly reliable communications whenever and wherever needed and (ii) efficient utilization of the radio spectrum.” Again, it is clear that to be aware, to learn, and to adapt according to some objectives are the key steps of the cognition cycle.
Several other definitions are present in the literature and in documents by standardization institution and an exhaustive discussion on these definitions can be found in .
In the vast spectrum of methodologies and applications that have occupied the radio context awareness area, herein we are considering those techniques that are more strictly related to the possibility of creating intelligent context aware applications rather than improving directly communication efficiency, namely, to those properties of wireless transmission and propagation that can provide some crucial capabilities to the context aware systems. In Figure 2, we classify the main functions that can arise from radio transmission for context awareness according to their main usage: (i) for the application and service layer or (ii) for the efficiency of the wireless transmission itself in terms of capacity, coverage, and/or opportunistic exploitation of the radio resources. We observe that this division is related to the exploitation of the three fundamental physical domains, that is, time, frequency, and space. Increasing awareness in the time and frequency domains of the environment opens new potential abilities of increasing the transmission efficiency since this is related to the enhanced knowledge of the signals (and possibly of interference) in the surrounding environment. On the other hand, all is related to enhanced knowledge of the space around the transmitters can produce either some advantages on the transmission efficiency or, more importantly, numerous applications and services for users, machines, and systems that are present and work in that surrounding environment. Therefore, the relation between electromagnetic propagation and space and the possibility of deriving space properties (in terms of existence or detection and identification, position, and velocity) of users and/or objects is the main contribution that radio transmission physical layer can provide to the application layer of context aware systems (Figure 2).
It is worth observing that this formulation of radio context awareness can be integrated also in the more recent evolutions of cognitive radio, in particular cognitive networks, where the environment cognition is extended from the physical and data link layers to the network and application ones. According to this research line, the wireless environment is enriched by other cognitive layers, firstly the network and user environments forming a sophisticated and cross layer so-called multidomain environment [20, 21].
Obviously, radio context awareness has also some costs and drawbacks, mainly regarding the increased complexity (in the device architecture and/or in the algorithms computational load), traffic (between the nodes in a WSN), and energy consumption. These challenging issues and drawbacks depend on the algorithmic solution chosen for achieving the context awareness measures. We can classify the algorithmic approaches according to their cooperative nature and to the distribution of the computational load. Firstly, in a cooperative approach, a network of sensors performs the observation and the measurement regarding the context parameter. Therefore, a large number of measures can be available for the context parameter estimate, with a clear performance advantage. In addition, cooperation might be a necessity, for example, when the WSN is composed by mobile nodes or when the single nodes do not have the capability of achieving a satisfactory context parameter estimate. The main drawback of a cooperative approach, especially in a WSN, is the energy consumption since more nodes are required to join the overall process; therefore, a compromise has to be achieved between the performance gain and the energy consumption. Secondly, an algorithm can be operated in a centralized or a distributed manner. In a centralized approach, all the nodes transmit the estimated measures to a single node, chosen according to the network topology or its technology and characterized by a large computational capability. Conversely, in a distributed approach, the algorithm is performed by a subset or by all the nodes, and the result is computed locally. The main advantage in a distributed approach is given by the reduction of the necessity of transmitting data to the unique central processor; in fact, in a distributed approach, nodes send measured data mainly to their neighbors, and the final measure is usually derived by successive refinements performed in an iterative way. So, centralized and distributed approaches have an impact on the network traffic, on the computational load, on the final performance, on the estimate latency, and on the energy costs. The main tradeoff to be analyzed is usually between the network traffic and the computational load in the network nodes. Also in this case, the energy consumption plays an important role since transmission is usually more expensive than data processing and computing, and hence reductions of data traffic can guarantee important advantages.
The next sections, dedicated to the areas of radio identification, detection, and positioning, respectively, present a brief review of the radio technologies that realize these functions, of the main practical applications and then a survey of the recent advances published in the literature.
3. Radio Identification
Radio identification relies on the possibility of exchanging automatically information about the identity of a person/object by means of radio transmission of short packets. Radio identification can occur by using active or passive systems, referred to as RFID systems: the former uses a power source for emitting a radio frequency signal while the latter can be read by means of electromagnetic induction at short ranges. RFID systems provide automatic identification and consist of small devices, called transponders or tags, which can be read or written by transceivers, called readers. Moreover, the system is usually completed by a database, which contains the data records linked to the identification messages sent by the tags (Figure 3). RFID systems can be used for identifying objects and persons in order to manage inventory and tracking in a huge variety of applications and fields, such as (i)tracking of persons and animals,(ii)logistics and tracking of objects (inventory, supply chains management, baggage control in airports, warehouses, commercial centers, industries, hospitals, waste control, and containers),(iii)authenticity control of goods (luxury goods, prescribed drugs),(iv)access control (building access, car access),(v)toll contactless payments and ticketing (subway, public transport systems, museums, and stadium).
The physical functioning principle is based on the passive or active interaction between tags and readers, which realizes a master-slave architecture. The tag contains electronically stored identification information which can be read at several distances (ranges from few centimeters to several meters away), not necessarily in line of sight, wireless conditions. Also the radio frequency bands are different, and they are related to several power and performance parameters; Table 1 reports the existing main standards with the corresponding operating frequencies.
While active tags operate by transmitting and receiving short packets with standard digital modulation formats, passive tags rely on a different physical mechanism for exchanging information with the transceiver. In this latter case, the tag transponder receives its power from the reader by magnetic induction: the transceiver sends an electromagnetic signal to the tag, the tag dipole antenna charges a capacitor, and then the capacitor releases the accumulated energy to the tag’s coil. Finally, the coil transmits a signal containing the encoded information that will be read by the transceiver. For a more detailed review of the RFID technology and applications, the reader can refer to [22–28].
In the context of radio identification, near field communication (NFC) includes technologies for the confluence between RFID and cellular telephony, particularly smartphones and similar devices [29, 30]. Radio communication is activated by close proximity (usually less than 10 cm) or contact between two mobile devices, and the most promising applications include contactless, fast payments and social networking. NFC is present in ISO/IEC 18092, ISO/IEC 21481 and ECMA-340, ECMA-352 standards (see Table 1): these standards specify the modulation, coding, frame format, and the initialization and transport protocols.
Next Section 3.1 summarizes the most recent results in this field through a selection of papers published in the last 3 years.
3.1. Recent Advances in Radio Identification
We have organized the selected publications into the following main research and development fields.
3.1.1. Antenna Design
The largest number of recent articles is dedicated to the design of the two fundamental elements of an RFID system, that is, the antennas at the reader and the tag [31–33]. The reader antenna should generate a strong magnetic field while tag antennas, usually subject to size and shape constraints, should also be robust against angular position and orientation. In , the authors present a narrow-band, small antenna, which is implemented by spraying it with conductive paint on a car chassis.
3.1.2. Tags on Metallic Surfaces
It is well known that RFID tags suffer from large performance degradation when attached to conductive surfaces. In [35, 36], the authors present two antenna designs that are suitable for installation on metallic objects.
In this context,  presents the design and fabrication of RFID tag antennas operating in the low microwave frequency range (3.5 GHz) on paper substrate and with an inkjet printing process; this technology assures very easy reproducibility, mechanical flexibility, and ultra-low costs.
Security is another current research subject since most existing RFID tags can be cloned or tracked. In , the authors implement and study RF fingerprinting for authenticating individual RFID tags at the physical layer; RF fingerprinting works by associating an RF waveform with its unique source, and, in an RFID tag, this requires finding these unique variations in its RF signature. In , the authors use combined techniques as dynamic wavelet fingerprint, wavelet packet decomposition, and higher order statistics, to obtain high levels of authentication accuracy.
A considerable number of articles are dedicated to RFID applications or design of RFID devices and systems for specific uses. We mention two fields that are really promising, robotic navigation and healthcare systems. In , the authors design specific RFID tags and devices for mobile robot navigation and localization, to face the typical problems that limit standard RFID technologies in this application, that is, size, weight, power consumption of transponders, effect of multipath, and dependence of RFID performance on the support surface. Therefore, ad hoc readers and tags are designed and implemented with a particular care to antennas design, optimized in terms of technology cost, radiation pattern, polarization, gain, and frequency requirements. This study confirms that RFID technology, being characterized by low cost and largely available technological solutions, offers the opportunity and flexibility of customizing specific solutions. In , the RFID application to healthcare is considered by means of the design, implementation, and testing of a system for automatic patient identification, real-time location, and tracking of medical assets and equipments, reliable drug inventory in an oncology hospital in Cyprus.
3.2. Research Challenges and Opportunities
The main research and technical challenges of radio identification regard the range and reliability performance in presence of harsh environments, especially characterized by metallic surfaces and/or multiple sources. Antenna design and technologies for miniaturization of these devices are part of a process that will bring RFID devices to be embedded invisibly in many objects since their production; this development is clearly related to the huge number of objects that will be connected to Internet in the future (IoT). Of course, energy harvesting and management techniques will continue to be a fundamental research and development subject for autonomous, passive or active, devices. Finally, security for preventing unauthorized access and misuse in this kind of simple and inexpensive networks will surely require a further research effort in the next future.
4. Radio Detection
A radio device can be designed for sensing variations in the received signal or, more significantly, in the channel impulse response. These fluctuations in the received signal are originated by variation of the radio wave propagation when additional passive objects and/or persons are introduced in a given environment. If radio localization (Section 5) is generally associated to location estimation of active transceivers, radio detection is associated to the detection of activities or subjects that do not participate in the radio transmission. Therefore, radio detection relies on the ability of sensing variations in the received radio signal generated by variations of the surrounding environment, responsible for specific scattering and reflection pattern of the electromagnetic waves. In this field, the technologies are usually based on the following parameters that can be derived from the received signal, with increasing levels of complexity: (i)received signal strength indicator (RSSI),(ii)channel delay and Doppler offset,(iii)channel impulse response. These parameters are usually estimated between two or more active, usually but not necessarily stationary, devices that monitor the environment. These devices, which can be also colocated as in the classical radar case, exchange radio signals in order to monitor the parameter variations. The channel impulse response is the most general approach for detecting modifications in the environment caused by the introduction of new elements in the environment or modifications of the existing ones; the underlying physical mechanism is simply the superimposition or modification of the multiple paths of the electromagnetic waves. Channel delay and Doppler frequency offset are usually exploited by radar applications, in which mobile objects are detected and possibly tracked by processing the reflected signals. On the other hand, variations in the RSSI are typically used for detecting intrusion or presence of new objects or persons in a given environment. Here, we are interested in the application of radio detection in indoor environments since this is the most challenging and innovative case, from an application point of view. Therefore, the main technologies involved in indoor applications are the WLAN and WPAN ones, namely, the standards IEEE 802.11 (e.g. WiFi) and IEEE 802.15 (e.g. Bluetooth and ZigBee).
If outdoor radar system is the main technology in the field of radio detection and tracking, the more recent indoor radar technology is acquiring increasing significance for its many applications. In the recent years, ultra wideband (UWB) technology has been extensively studied in this field for its interesting properties in terms of time and consequently channel response resolution. UWB impulse radio (UWB-IR) makes use of ultrashort duration pulses which yield ultrawide bandwidth signals characterized by extremely low power spectral densities. These signals can be used as an overlay technology for short-range wireless communications, and they potentially combine immunity to multipath fading, low probability of intercept (LPI), low power consumption, and high time resolution. According to the IEEE 802.15.4a standardization (Figure 1), the minimum allocated channel bandwidth is 499.2 MHz, and the UWB-IR transmission is centered around carriers between 3 and 5 GHz and between 6 and 10 GHz.
In the last years, great interest has attracted also the through-the-wall radar imaging (TWRI) technology, which exploits the penetration of wide-band signals through obstacles for detecting and discriminating persons and objects separated by walls with respect to the transceivers. This technology clearly suffers from significant wall attenuation, increased multipath and scattering effects, and larger false alarms probabilities with respect to standard indoor radar. These impairments are usually mitigated by increasing the number of sensors, usually organized in arrays for exploiting beamforming techniques and optimizing their positions outside the area to be scanned; nevertheless, the physical parameters used for detection are usually Doppler signatures and signal variations as well.
The main applications of radio detection are summarized in the following fields:(i)intrusion detection of persons or animals,(ii)in the electronic health (eHealth) context, some technologies can be used for detecting abnormal status of some patients’ parameters, for example, breathing, (iii)motion detection of objects or persons.
4.1. Recent Advances in Radio Detection
This section is dedicated to a survey of the main advances in this field through some selected publications of the last 3 years, situated in the following fields.
4.1.1. Activities or Intrusion Detection
In , we find an example of activities detection from the analysis of modifications in the RF channels. The authors classify activities of noncooperating (i.e., nontransmitting) subjects, as walking, lying, crawling, or standing persons, and they detect them achieving a localization accuracy within 1 meter. References [42, 43] present examples of human and intrusion detection systems based on monitoring the RSSI variations in indoor environments, with a remark on alternative applications, such as energy savings in residential environments. Finally,  shows the implementation of an RF sensor for shoe-based navigation that can measure its position with respect to the surface with a precision better than 1.3 mm and detect a person motion as slow as 191 μm/s; this result is a considerable improvement with respect to standard inertial sensors.
4.1.2. Vital Signs Detection
Detection of signs related to breathing and heart frequency by means of RF signals is attracting a considerable interest in the healthcare field, especially for the noninvasive nature of these systems. Automatic healthcare monitoring systems might be really effective especially for ageing population and very early symptom detection in heart attacks or some chronic diseases. Sensors presented in [45, 46] are based on the properties of UWB-IR technology (large bandwidth and high time resolution) while [47, 48] are based on a wearable Doppler radar at 2.45 GHz and an environment activity detection system at 5.8 GHz, respectively.
4.1.3. Indoor Radar
In , a navigation system based on UWB, which permits mobile robot navigation in indoor environments in a more accurate way with respect to existing sensors technologies, as odometers and sonars, is proposed. This confirms the advantages in terms of obstacle detection of UWB signals, thanks to their high time and space resolution. Then,  presents a UWB radar system, able to integrate camera-based surveillance systems, which exploits a low number of antennas (five) for estimating the target motion, rotation, and shape with an accuracy level significantly greater than conventional schemes based on antenna arrays at the same transmission frequencies.
4.1.4. Through the Wall Imaging
The system proposed in  improves the robustness to the mentioned TWRI impairments (Section 4) by means of a sophisticated target classification; the image is divided into segmented objects which are classified according to features like material, shape, and so forth. The peculiarity of this target classification is that measured features, statistical and geometrical, are resolution and position independent, and this property enforces robustness and performance of the high resolution TWRI based on antenna arrays. On the other hand,  is focused on an adaptive detection scheme, which does not rely on image statistics a priori knowledge, but it updates and adapts its parameters during the iterative detection process.
4.2. Research Challenges and Opportunities
The main research and technical challenges of radio detection regard the achievement of satisfactory performance in presence of difficult propagation environments, in particular NLoS and/or with multiple, potentially interfering targets. In the typical propagation conditions of indoor radar and TWRI implementations, also clutter phenomena complicate management of false alarm events. Finally, the possibility of enhancing information acquired during detection of passive objects (e.g., regarding the shape and rotation) is a challenging and interesting opportunity especially in presence of sensor systems with limited complexity.
Wireless localization has been a topic of great and increasing interest in the last years since it opens a wide spectrum of advanced applications with a crucial role in the near-future wireless markets. In all the wireless systems of different scales (Figure 1), from short range WPAN to large range cellular mobile systems (wide area networks of the 2nd, 3rd and 4th generation), the localization of fixed/moving devices (or targets) is obtained by exchanging radio signals among reference base stations placed in known positions (called beacons or anchors) and the targets in unknown positions.
Localization can be exploited in a large spectrum of applications and fields, such as manufacturing, transport, vehicular ad hoc networks, medicine, agriculture, and tourism, just to mention a few. The main applications of the localization capability of properly designed radio systems can be categorized as follows. (i)Location-aware services. (ii)Emergency situations. (iii)Security. (iv)Environmental monitoring. (v)Positioning and tracking of objects, persons, and animals.
The target coordinates are computed by using a preliminary estimation of some physical parameters that can be derived from the received signals. The typical parameters for localization are in the following list. (i)Time of arrival (TOA) and time difference of arrival (TDOA). (ii)Angle of arrival (AOA) or direction of arrival (DOA). (iii)Received signal strength (RSSI). The RSS measures are inexpensive and simple while the high-resolution TOA-based measures (especially in ultra wideband systems) can require relevant resources in terms of sampling rate and computational complexity. The estimate precision of these parameters depends mainly on the signal-to-noise ratio. In addition, times of arrival are influenced by the signal bandwidth (time resolution is inversely proportional to the bandwidth), non-line of sight (NLoS) channel conditions (which introduce an offset on the propagation times), and multipath. Angles of arrival can be estimated by using arrays of sensors or devices, and hence they are primarily influenced also by the number of array elements while RSSI is greatly affected by large scale shadowing and small scale fading effects, which affect the relation with the real propagation distance.
These parameters are then combined by means of different estimation approaches, which mainly apply the classical estimation methods and exploit geometric multilateration/angulation constraints. These algorithms achieve, usually in a suboptimal and iterative way, the minimization of a cost function related to the target coordinate errors. Therefore, the relations between these parameters and the target coordinates are given either by analytical models or by field measurements, commonly referred to as fingerprinting techniques or digital maps. A great research effort has been spent also in the integration among different measure parameters through the use of data fusion and hybrid localization algorithms. Figure 4 illustrates a typical scenario in which some target nodes should be located in presence or absence of fixed beacons. It is clear that, in absence of fixed beacons with known positions, only the relative locations of the existing targets will be computed while the presence of a sufficient number of beacons with known positions allows the determination of the targets absolute coordinates. It is also important to remark that the localization performance is determined either by the number of nodes that are involved in the localization process or their locations. Figure 4 and Table 2 report the main concepts that characterize the approaches for determining the location estimates, according to different modes for involving the network nodes in the process. Firstly, the principle of cooperative localization opens one of the fundamental design choices in this context: in a cooperative approach, also nodes with unknown positions, or targets, cooperate with the positioning algorithm, that is, by allowing nodes in unknown locations to exchange measurements on a peer-peer basis (Figure 4(a)). In a noncooperative approach, the targets exchange measurements only with the beacons (Figure 4(b)). In addition, localization can take advantage of the integration of different technologies in the nodes (e.g., satellite global positioning, cellular, and WLAN systems). Also centralized and distributed implementations require careful design compromises in terms of traffic, computational load, and energy budget, as already observed in Section 2.
Finally, many localization approaches are based on standard estimation techniques: non-Bayesian approaches, as maximum likelihood estimation (MLE), classical gradient-descent methods least squares (LS), and weighted LS (WLS), and Bayesian approaches, as maximum a posteriori (MAP), minimum mean squared error (MMSE), or exploiting particle filters. Other methods for minimizing the cost functions are based on genetic algorithms, applied mainly to centralized solutions.
Of course, the localization concept is completed by the tracking that includes the ability to treat the correlation between consecutive target positions inside a trajectory. A detailed review of the localization algorithms can be found in [53–62].
5.1. Recent Advances in Radio Localization
This section is dedicated to a survey of the main advances in this field published in the last 3 years. The selected publications, which provide also a view on the most promising current research areas, are situated in the following fields.
5.1.1. Performance Limits
Considerable effort has been spent for investigating the theoretical performance bounds of localization algorithms; this problem is complicated since localization accuracy depends either on the link transmission quality or on the network topology. References [63, 64] present fundamental limits of localization performance, in terms of squared position error bound (SPEB), in wideband wireless networks and multipath environments. Reference  is dedicated to the general theoretical framework, which is based on the notion of equivalent Fisher information and developed on the received signal waveforms instead of signal metrics, such as time-of-arrival and received signal strength. Reference  investigates the limits of cooperative location-aware networks, providing a geometrical interpretation of equivalent Fisher information for cooperative networks (with presence of beacons and targets) and deriving performance limits with their scaling properties. Then,  exploits an interesting concept: the localization of a mobile device is modeled in the framework of Shannon capacity communication bounds, modeling the location as a transmitted message encoded by propagation conditions. This approach allows the determination of information theoretic bound in urban environments, which provides conditions for achieving arbitrary small probability of the localization error. The work  investigates the performance limits for RSSI based localization analyzing the corresponding Fisher information and the Cramér-Rao bounds (CRB). Finally,  presents an iterative maximum likelihood solution to sensor localization in presence of nonlinear propagation of localization error in the sensor network, as in radio interferometric positioning systems (where radio frequency interference is used for obtaining sum of distance differences between the locations of four devices). The proposed method is optimal since it achieves the CRB.
5.1.2. Hybrid Systems
Hybrid systems rely on the combination or fusion of measures from different techniques for achieving or improving localization. In , the authors propose a distributed, cooperative positioning algorithm that exploits positioning measures from satellites and terrestrial wireless systems and works by an iterative message passing algorithm; an interesting contribution of the paper is the construction of a factor graph suitable for the application of the sum-product algorithm to hybrid positioning. The work presented in  faces a relevant issue that is often neglected in many articles on localization: the authors present a framework for hybrid fusion based on the determination and dynamic updating of the likelihood functions that represent the relation between measurements and distances. This operation allows the use of an adaptive estimation procedure based on particle filters, which is able to address the nonlinear and non-Gaussian nature of the localization problem, producing performance results very close to the CRBs. The recent work in  is devoted to a concrete example of hybrid measurement fusion for improving localization performance: the algorithm uses pyroelectric infrared (a pyroelectric infrared sensor detects motion and human or animal presence through sensing infrared radiation that depends on the target temperature) and radio RSSI measures and has shown interesting experimental results also in presence of multiple targets. Finally,  presents an idea for improving the accuracy of localization, also in presence of hybrid data sources, by using Kalman filtering for preprocessing noisy distance measurements gathered in difficult environments and then a cooperative spring relaxation for refining the nodes initial coarse position estimates.
5.1.3. Heterogeneous Systems
In this field, the hybrid fusion of different measures is achieved by integrating measures from different radio technologies or systems. The presence of multiple, heterogeneous wireless infrastructures and the increasing demand of localization services, either outdoor or indoor, open new opportunities for the integration of these systems. In , the author presents a cooperative approach that is able to combine RSSI measures from different technologies according to different weights representing the information contribution of each measure. This algorithm is tested with real measurements from GSM, DVB, FM, and WLAN systems in outdoor and indoor environments. In , the fusion approach is extended to a GSM multiple provider scenario, showing that a cross-provider positioning algorithm (e.g., by means of a dual SIM phone) provides a significant accuracy improvement. Finally, since indoor global navigation satellite system (GNSS) localization is generally not viable, the feasibility of peer-to-peer cooperative positioning has been recently tested in such a context [74–76]; as in the augmented-GNSS technique (A-GNSS), this innovative strategy helps the user to increase the terminal sensitivity.
Fingerprinting techniques determine a device position by comparing its RSSI measures with stored RSSI maps, and they are widespread especially in WiFi systems, where the RSSI measure is the only estimate available in the standard compliant receivers. The work in  is dedicated to the critical design of an effective RSSI-location mapping function, which is derived by improving the diffused projection techniques known as principal component analysis (PCA) and multiple discriminant analysis (MDA) by means of a dynamic hybrid projection approach. Then,  shows how the expensive map construction phase can be conducted by a collaborative system in which anonymous mobile smartphone users automatically collect and send data regarding an indoor area to a server that progressively creates a fingerprinting map with a satisfactory accuracy level. Finally,  presents the application of a particle filter approach with a likelihood estimation mechanism to real data obtained from a WiMAX network in the city of Brussels (Belgium).
The RSSI measures are typically less expensive and less precise with respect to the others, and they are often obtained with some simplified assumptions on the noise and channel environment. In , the authors propose some novel and more rigorous probabilistic models for RSSI, accounting for background noise and their corresponding maximum likelihood estimators. Then, it is worth mentioning that, in , an improved linear least squares estimator for RSSI-based positioning is shown to achieve the CRB at medium-high SNRs.
5.1.6. Multitarget Localization
Localization and tracking of multiple targets is another challenging issue because of the limits imposed by spatial resolution. The recent paper  proposes a novel technique for multitarget simultaneous localization and mapping (MSLAM), based on the combination between a particle filter and a truncated unscented Kalman filter; the algorithms perform better than previous SLAM approaches in multitarget scenarios.
5.1.7. Optical Localization
An interesting localization technology, usually referred to as optical wireless indoor localization, is based on light emitting diodes (LEDs), and it works on the same principles of the microwave versions [83, 84]: the phase differences are translated into time differences of arrival (TDOA), and it is interesting to remark that the modulation bandwidth of white LEDs used for indoor lighting is compatible with typical localization indoor applications.
5.1.8. RFID Localization
Of course, also RFID technology can be used or integrated in localization applications. In , the proposed algorithm, whose inputs are backscattered signal RSSIs from RFID tags and a tag-path position database, uses Kalman filters for tracking the reader position and incorporates an angle-dependent path loss factor for improving the final performance. In , performance of an RSSI human tracking system based on RFID badges is greatly improved by incorporating the impact of the human body on the badge antenna gain and directivity; to this purpose, a model of the directionality of a transmitter badge worn by a person is developed and included in the tracking process. Finally,  is focused on another application field of great interest: the presented method enables self-recognition of a moving vehicle position in harsh environments (e.g., industries and storage warehouses) with two RFID readers that return the RSSI measures by the mounted RFID tag.
5.1.9. Context Awareness
The work in  regards the relation between performance of a communication and/or localization system and its operational environment. The authors propose a context-driven hierarchical classification scheme for classifying the global positioning system localization accuracy according to different environment conditions; in fact, this aspect has a considerable impact on performance, and the ability to classify the measurement conditions would allow the target to use the localization estimates with an improved awareness in its decision strategy.
5.2. Research Challenges and Opportunities
Among the research challenges and opportunities in this field, indoor and multitarget localizations represent a promising field, especially, if we consider real and diffused applications in public spaces where the available networks and devices are currently not specifically designed for this task (WiFi networks and smartphones). Also, difficult propagation conditions (time-variant multipath, fading conditions, NLoS channels, and interference) are still an open research issue for this application, especially, when implemented systems have limited complexity. Finally, energy optimization in localization and tracking systems, that is, the optimal power allocation among the cooperating devices, is another challenging issue, relevant to applications in WSNs with battery powered sensors.
In this paper, we have reviewed the main radio techniques suitable for application in the field of context awareness, highlighting the main technologies, and the standards and the basic mechanisms at the physical layers. In the three main fields, radio identification, detection, and localization, we have reviewed the recent advances published in the last three years, hence remarking the challenges, the opportunities, and the next research and development steps in the future wireless networks for context aware systems.
- A. K. Dey and G. D. Abowd, “Towards a better understanding of context and context-awareness,” in Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing (HUC '99), pp. 304–307, Springer, 1999.
- T. Yücek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116–130, 2009.
- I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: a survey,” Physical Communication, vol. 4, no. 1, pp. 40–62, 2011.
- H. Celebi and H. Arslan, “Enabling location and environment awareness in cognitive radios,” Computer Communications, vol. 31, no. 6, pp. 1114–1125, 2008.
- S. Yarkan and H. Arslan, “Exploiting location awareness toward improved wireless system design in cognitive radio,” IEEE Communications Magazine, vol. 46, no. 1, pp. 128–136, 2008.
- H. Celebi, I. Güvenç, S. Gezici, and H. Arslan, “Cognitive-radio systems for spectrum, location, and environmental awareness,” IEEE Antennas and Propagation Magazine, vol. 52, no. 4, pp. 41–61, 2010.
- F. K. Jondral, “Cognitive radio: a communications engineering view,” IEEE Wireless Communications, vol. 14, no. 4, pp. 28–33, 2007.
- L. M. Correia, D. Zeller, O. Blume et al., “Challenges and enabling technologies for energy aware mobile radio networks,” IEEE Communications Magazine, vol. 48, no. 11, pp. 66–72, 2010.
- B. Wang and K. J. R. Liu, “Advances in cognitive radio networks: a survey,” IEEE Journal on Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011.
- J. Wang, M. Ghosh, and K. Challapali, “Emerging cognitive radio applications: a survey,” IEEE Communications Magazine, vol. 49, no. 3, pp. 74–81, 2011.
- S. Morosi, T. Bianchi, and F. Gei, “Frequency domain multiuser receivers for an IEEE 802. 15. 4a short range communication network,” Transactions on Emerging Telecommunications Technologies.
- S. Morosi, P. Piunti, and E. del Re, “Sleep mode management in cellular networks: a traffic based technique enabling energy saving,” Transactions on Emerging Telecommunications Technologies, vol. 24, no. 3, pp. 331–341, 2013.
- L. Atzori, A. Iera, and G. Morabito, “The Internet of things: a survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
- L. Mainetti, L. Patrono, and A. Vilei, “Evolution of wireless sensor networks towards the Internet of Things: a survey,” in Proceedings of the 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM '11), pp. 16–21, September 2011.
- J. Mitola, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio [Ph.D. thesis], Royal Institute of Technology, Stockholm, Sweden, 2000.
- J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999.
- J. M. Peha, “Sharing spectrum through spectrum policy reform and cognitive radio,” Proceedings of the IEEE, vol. 97, no. 4, pp. 708–719, 2009.
- S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
- J. O. Neel, Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms [Ph.D. thesis], Virginia Polytechnic Institute and State University, 2006.
- G. Ding, J. Wang, Q. Wu, and Y. Gong, “System info of multi-domain cognition in cognitive radio networks,” in Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP '10), pp. 1–4, October 2010.
- Z. Wenzhu and Y. Bohai, “Approach for local multi-domain cognition in cognitive network,” Communications, China, vol. 10, no. 1, pp. 146–156, 2013.
- S. Preradovic, N. C. Karmakar, and I. Balbin, “RFID transponders,” IEEE Microwave Magazine, vol. 9, no. 5, pp. 90–103, 2008.
- K. V. S. Rao, P. V. Nikitin, and S. F. Lam, “Antenna design for UHF RFID tags: a review and a practical application,” IEEE Transactions on Antennas and Propagation, vol. 53, no. 12, pp. 3870–3876, 2005.
- R. Want, “An introduction to RFID technology,” IEEE Pervasive Computing, vol. 5, no. 1, pp. 25–33, 2006.
- L. Zhu and T.-S. P. Yum, “A critical survey and analysis of RFID anti-collision mechanisms,” IEEE Communications Magazine, vol. 49, no. 5, pp. 214–221, 2011.
- V. P. Plessky and L. M. Reindl, “Review on SAW RFID tags,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 57, no. 3, pp. 654–668, 2010.
- X. Zhu, S. K. Mukhopadhyay, and H. Kurata, “A review of RFID technology and its managerial applications in different industries,” Journal of Engineering and Technology Management, vol. 29, no. 1, pp. 152–167, 2012.
- E. Ilie-Zudor, Z. Kemény, F. van Blommestein, L. Monostori, and A. van der Meulen, “A survey of applications and requirements of unique identification systems and RFID techniques,” Computers in Industry, vol. 62, no. 3, pp. 227–252, 2011.
- J. Romero, “No more waiting on near field communication,” IEEE Spectrum, vol. 49, no. 6, 64 pages, 2012.
- R. Want, “Near field communication,” IEEE Pervasive Computing, vol. 10, no. 3, pp. 4–7, 2011.
- J. Garcia, A. Arriola, F. Casado, X. Chen, J. Sancho, and D. Valderas, “Coverage and read range comparison of linearly and circularly polarised radio frequency identification ultra-high frequency tag antennas,” IET Microwaves, Antennas and Propagation, vol. 6, no. 9, pp. 1070–1078, 2012.
- Z. Xing, L. Wang, C. Wu, and K. Wei, “Study of broadband near-field antenna for ultra-high-frequency radio frequency identification applications,” IET Microwaves, Antennas and Propagation, vol. 5, no. 14, pp. 1661–1669, 2011.
- C. Cho, J. Ryoo, I. Park, and H. Choo, “Design of a novel ultra-high frequency radio-frequency identification reader antenna for near-field communications using oppositely directed currents,” IET Microwaves, Antennas and Propagation, vol. 4, no. 10, pp. 1543–1548, 2010.
- I. Zuazola, A. Sharma, J. Batchelor et al., “Radio frequency identification miniature interrogator antenna sprayed over an in-vehicle chassis,” IET Microwaves, Antennas and Propagation, vol. 6, no. 15, pp. 1674–1680, 2012.
- R. C. Hadarig, M. E. de Cos, Y. ÁLvarez, and F. Las-Heras, “Novel bow-tie antenna on artificial magnetic conductor for 5.8 GHz radio frequency identification tags usable with metallic objects,” IET Microwaves, Antennas and Propagation, vol. 5, no. 9, pp. 1097–1102, 2011.
- H.-G. Cho, N. R. Labadie, and S. K. Sharma, “Design of an embedded-feed type microstrip patch antenna for UHF radio frequency identification tag on metallic objects,” IET Microwaves, Antennas and Propagation, vol. 4, no. 9, pp. 1232–1239, 2010.
- G. Orecchini, F. Alimenti, V. Palazzari, A. Rida, M. M. Tentzeris, and L. Roselli, “Design and fabrication of ultra-low cost radio frequency identification antennas and tags exploiting paper substrates and inkjet printing technology,” IET Microwaves, Antennas and Propagation, vol. 5, no. 8, pp. 993–1001, 2011.
- C. Bertoncini, K. Rudd, B. Nousain, and M. Hinders, “Wavelet fingerprinting of radio-frequency identification (RFID) tags,” IEEE Transactions on Industrial Electronics, vol. 59, no. 12, pp. 4843–4850, 2012.
- L. Catarinucci, S. Tedesco, and L. Tarricone, “Customized ultra high frequency radio frequency identification tags and reader antennas enabling reliable mobile robot navigation,” IEEE Sensors Journal, vol. 13, no. 2, pp. 783–791, 2013.
- A. Polycarpou, A. Dimitriou, A. Bletsas et al., “On the design, installation, and evaluation of a radio-frequency identification system for healthcare applications,” IEEE Antennas and Propagation Magazine, vol. 54, no. 4, pp. 255–271, 2012.
- S. Sigg, M. Scholz, S. Shi, Y. Ji, and M. Beigl, “RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals,” IEEE Transactions on Mobile Computing, 2013.
- B. Mrazovac, M. Z. Bjelica, D. R. Kukolj, B. M. Todorović, and N. Teslic, “A human detection method f or residential smart energy systems based on Zigbee RSSI changes,” in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE '12), pp. 110–111, January 2012.
- O. Kaltiokallio and M. Bocca, “Real-time intrusion detection and tracking in indoor environment through distributed RSSI processing,” in Proceedings of the 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA '11), pp. 61–70, August 2011.
- C. Zhou, J. Downey, J. Choi, D. Stancil, J. Paramesh, and T. Mukherjee, “A shoe-embedded RF sensor for motion detection,” IEEE Microwave and Wireless Components Letters, vol. 21, no. 3, pp. 169–171, 2011.
- T. Akiyama, T. Ikegami, and K. Takizawa, “A non-invasive detection of respiration by using ultra wideband signals,” in Proceedings of the 15th International Symposium on Wireless Personal Multimedia Communications (WPMC '12), pp. 514–518, 2012.
- A. Lazaro, D. Girbau, R. Villarino, and A. Ramos, “Vital signs monitoring using impulse based UWB signal,” in Proceedings of the 41st European Microwave Conference (EuMC '11), pp. 135–138, October 2011.
- R. R. Fletcher and S. Kulkarni, “Wearable doppler radar with integrated antenna for patient vital sign monitoring,” in Proceedings of the IEEE Radio and Wireless Symposium (RWS '10), pp. 276–279, January 2010.
- G. Reyes, D. Wang, R. Nair, C. Li, X. Li, and J. Lin, “VitalTrack: a doppler radar sensor platform for monitoring activity levels,” in Proceedings of the IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS '12), pp. 29–32, January 2012.
- M. Segura, V. Mut, and C. Sisterna, “Ultra wideband indoor navigation system,” IET Radar, Sonar and Navigation, vol. 6, no. 5, pp. 402–411, 2012.
- T. Sakamoto and T. Sato, “Two-dimensional ultrawideband radar imaging of a target with arbitrary translation and rotation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4493–4502, 2011.
- C. Debes, J. Hahn, A. M. Zoubir, and M. G. Amin, “Target discrimination and classification in through-the-wall radar imaging,” IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 4664–4676, 2011.
- C. Debes, J. Riedler, A. M. Zoubir, and M. G. Amin, “Adaptive target detection with application to through-the-wall radar imaging,” IEEE Transactions on Signal Processing, vol. 58, no. 11, pp. 5572–5583, 2010.
- N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero III, R. L. Moses, and N. S. Correal, “Locating the nodes: cooperative localization in wireless sensor networks,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 54–69, 2005.
- J. Zhou and J. Shi, “RFID localization algorithms and applications a review,” Journal of Intelligent Manufacturing, vol. 20, no. 6, pp. 695–707, 2009.
- G. Mao, B. Fidan, and B. D. Anderson, “Wireless sensor network localization techniques,” Computer Networks, vol. 51, no. 10, pp. 2529–2553, 2007.
- J. Wang, R. Ghosh, and S. Das, “A survey on sensor localization,” Journal of Control Theory and Applications, vol. 8, no. 1, pp. 2–11, 2010.
- S. Gezici and H. V. Poor, “Position estimation via ultra-wide-band signals,” Proceedings of the IEEE, vol. 97, no. 2, pp. 386–403, 2009.
- H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 37, no. 6, pp. 1067–1080, 2007.
- I. Güvenç and C.-C. Chong, “A survey on TOA based wireless localization and NLOS mitigation techniques,” IEEE Communications Surveys and Tutorials, vol. 11, no. 3, pp. 107–124, 2009.
- D. Macagnano, G. Destino, and G. Abreu, “A comprehensive tutorial on localization: algorithms and performance analysis tools,” International Journal of Wireless Information Networks, vol. 19, no. 4, pp. 290–314, 2012.
- Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 13–32, 2009.
- S. Gezici, “A survey on wireless position estimation,” Wireless Personal Communications, vol. 44, no. 3, pp. 263–282, 2008.
- Y. Shen and M. Z. Win, “Fundamental limits of wideband localization. Part I: a general framework,” IEEE Transactions on Information Theory, vol. 56, no. 10, pp. 4956–4980, 2010.
- Y. Shen, H. Wymeersch, and M. Z. Win, “Fundamental limits of wideband localization. Part II: cooperative networks,” IEEE Transactions on Information Theory, vol. 56, no. 10, pp. 4981–5000, 2010.
- I. Bilik, K. Adhikari, and J. R. Buck, “Shannon capacity bound on mobile station localization accuracy in urban environments,” IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 6206–6216, 2011.
- G. Giorgetti, S. K. S. Gupta, and G. Manes, “Understanding the limits of RF-based collaborative localization,” IEEE/ACM Transactions on Networking, vol. 19, no. 6, pp. 1638–1651, 2011.
- Y. Cheng, X. Wang, T. Caelli, X. Li, and B. Moran, “Optimal nonlinear estimation for localization of wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 5674–5685, 2011.
- M. A. Caceres, F. Penna, H. Wymeersch, and R. Garello, “Hybrid cooperative positioning based on distributed belief propagation,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 10, pp. 1948–1958, 2011.
- J. Prieto, S. Mazuelas, A. Bahillo, P. Fernández, R. M. Lorenzo, and E. J. Abril, “Adaptive data fusion for wireless localization in harsh environments,” IEEE Transactions on Signal Processing, vol. 60, no. 4, pp. 1585–1596, 2012.
- R. C. Luo and O. Chen, “Wireless and pyroelectric sensory fusion system for indoor human/robot localization and monitoring,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 3, pp. 845–853, 2013.
- B.-C. Seet, Q. Zhang, C. H. Foh, and A. C. M. Fong, “Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1427–1435, 2012.
- S.-H. Fang and T.-N. Lin, “Cooperative multi-radio localization in heterogeneous wireless networks,” IEEE Transactions on Wireless Communications, vol. 9, no. 5, pp. 1547–1551, 2010.
- S. H. Fang, “Cross-provider cooperation for improved network-based localization,” IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 297–305, 2013.
- M. Panizza, C. Sacchi, J. Varela-Miguez et al., “Feasibility study of a SDR-based reconfigurable terminal for emergency applications,” in Proceedings of the IEEE Aerospace Conference (AERO '11), pp. 1–5, March 2011.
- S. Morosi, E. Del Re, and A. Martinelli, “P2P cooperative GPS positioning with fine/coarse time assistance,” in Proceedings of the International Conference on Localization and GNSS (ICL-GNSS '13), pp. 1–5, 2013.
- M. Berioli, A. Molinaro, S. Morosi, and S. Scalise, “Aerospace communications for emergency applications,” Proceedings of IEEE, vol. 99, no. 11, pp. 1922–1938, 2011.
- S.-H. Fang and C.-H. Wang, “A dynamic hybrid projection approach for improved Wi-Fi location fingerprinting,” IEEE Transactions on Vehicular Technology, vol. 60, no. 3, pp. 1037–1044, 2011.
- Y. Kim, Y. Chon, and H. Cha, “Smartphone-based collaborative and autonomous radio fingerprinting,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 42, no. 1, pp. 112–122, 2012.
- M. Bshara, U. Orguner, F. Gustafsson, and L. van Biesen, “Fingerprinting localization in wireless networks based on received-signal-strength measurements: a case study on WiMAX networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 1, pp. 283–294, 2010.
- R. Martin, A. King, J. Pennington, R. Thomas, R. Lenahan, and C. Lawyer, “Modeling and mitigating noise and nuisance parameters in received signal strength positioning,” IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 5451–5463, 2012.
- H. C. So and L. Lin, “Linear least squares approach for accurate received signal strength based source localization,” IEEE Transactions on Signal Processing, vol. 59, no. 8, pp. 4035–4040, 2011.
- Á. F. García-Fernández, M. R. Morelande, and J. Grajal, “Multitarget simultaneous localization and mapping of a sensor network,” IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 4544–4558, 2011.
- S.-Y. Jung, S. Hann, and C.-S. Park, “TDOA-based optical wireless indoor localization using LED ceiling lamps,” IEEE Transactions on Consumer Electronics, vol. 57, no. 4, pp. 1592–1597, 2011.
- K. Panta and J. Armstrong, “Indoor localisation using white LEDs,” Electronics Letters, vol. 48, no. 4, pp. 228–230, 2012.
- S. S. Saad and Z. S. Nakad, “A standalone RFID indoor positioning system using passive tags,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1961–1970, 2011.
- Y. Zhao, N. Patwari, P. Agrawal, and M. Rabbat, “Directed by directionality: benefiting from the gain pattern of active RFID badges,” IEEE Transactions on Mobile Computing, vol. 11, no. 5, pp. 865–877, 2012.
- S. Park and H. Lee, “Self-recognition of vehicle position using UHF passive RFID tags,” IEEE Transactions on Industrial Electronics, vol. 60, no. 1, pp. 226–234, 2013.
- N. Drawil, H. Amar, and O. Basir, “GPS localization accuracy classification: a context-based approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 262–273, 2013.