Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as Wireless Sensor Networks (WSN) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location based services (LBS). The development of sensor technology, processing techniques and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization namely self determining and training dependent approaches. Finally, various open research issue related to localization schemes for IoT are compared and proposed various directions for future research.

The ’Smart World’ envisioned by technology will be achieved by the penetration of intelligence into ubiquitous things including physical objects, cyber-entities, social-elements or individuals and human thinking. The development of Smart World is enabled by diverse applications of Wireless Sensor Networks (WSN) into those components identified as things. Such a smart-world will have features controlled significantly by the location information. Control and Policy information of Smart World services, often addressed as Location-Based Services (LBS), are governed by location data. Localization thus becomes the key enabling technology for Smart World facilities. It is generally classified as active and passive techniques in nature. Active localization is a widely adopted localization scheme where the target is detected and tracked carries a tag or attached device. The other category, Passive methods, defines targets to be localized as free of carrying a tag or device, hence also referred to as Device-Free localization (DFL) or Sensor-less localization. The passive approach is a well suited for the development of diverse smart world applications with ubiquitous localization. Device-free localization schemes fall into a wide range of application scenarios within the Smart World ecosystem. A few notable examples are occupancy detection, identity definition, positioning, gesture detection, activity monitoring, pedestrian and vehicle-traffic flow surveillance, security safeguarding, ambient intelligence-based systems, emergency rescue operations, smart work-spaces and patient or elderly monitoring. In this paper, the revolution of device-free localization technologies have been reviewed and classified comprehensively. Further, the emergence of the Smart World paradigm is analyzed in the context of device-free localization principles. Moreover, the inherent challenges within the application domains have been extensively discussed and improvement strategies for multi-target localization and counting approach are discussed. Finally, current trends and future research directions have been presented.

Connected vehicles and fully automated driving systems are the main objectives of the future transportation system. A safe interactive system that interacts with people and things is essential to achieve these objectives. In this context, a crowd intelligence system plays a key role in interactive system development. Crowd intelligence is a combined method of data collection, integration and analysis from devices such as the smartphones, wearables, vehicles and a wide range of Internet of Things applications to use them as sensors. This collective feedback‐driven interactive method is opportunistic for the development of the future transportation system. In this study, a survey is conducted considering crowd‐intelligence techniques for the transportation system. From this survey, various challenges of the intelligent transportation system have been outlined and crowd‐intelligent solutions have been discussed. A layered structure of transportation system architecture is suggested considering various problems in each layer and its crowd‐intelligent solutions. The crowd‐intelligence‐based mobility, traffic control, traffic prediction, parking solutions have been discussed in this survey. Moreover, the importance of crowd‐intelligent techniques and its applicability is discussed for sustainable development of futuristic transport infrastructure.

The localization accuracy is critical for the development of future autonomous systems and location-based services. The accuracy level for localization is difficult to achieve in the case of urban and GPS denied environments due to high scattering. Fingerprint-based localization techniques promise to address these challenges. However, this technique demands to build a radio map before localization, which is a time-consuming and labor-intensive task. This article designs a crowd-sourced based localization system to address the radio map building problem in fingerprinting localization system. In this method, the first initial radio map is constructed from the path-loss RSS model, followed by the update of the fingerprints with crowd-sourcing. Finally, the vehicle location is estimated from the RSS sample by matching it with an updated radio map with a deep learning algorithm. The main advantage of the proposed approach is the calibration-free crowd-sourced fingerprint generation and its applicability in various location-based services in urban infrastructure.

High electron mobility transistors (HEMTs) play a crucial role in microwave low‐noise amplifier (LNA) and are used in radar receiver, software defined radio and digital radio frequency. A novel technique is used to design and fabricate a high‐performance wideband LNA for 5.4–5.9 GHz based on fully stabilised InGaAs pseudo‐morphic HEMT (pHEMT) 0.15 µm technology. With the Agilent Advanced Design System simulation tool, a C‐band (5.4–5.9 GHz) two‐stage LNA using pHEMT based on monolithic microwave integrated circuit (MMIC) technology has been designed: noise figure 13 dBm and OIP3 >24 dBm, lower value of input/output return loss reflects the accuracy of impedance matching network at input and output sides of amplifier, full band unconditional stability. In this study, it is shown that the InGaAs pHEMT has the ability to handle high power to make it the perfect technology candidate for highly survival radar receiver component and survival up to 37 dBm input power level is demonstrated. The circuit based in the proposed technology shows comparable low noise figure, decent gain, with high dynamic range and high survivability. Finally, the simulation results and fabricated device results are in good agreement and superior than the earlier reported design.