This article presents a 12-bit frequency coded chipless RFID system in the frequency range of 3 to 6 GHz. The system consists of a fully printable chipless tag and a pair of high-gain reader antennas. The tag also incorporates its own antennas to improve the read range. Information is encoded into frequency spectrum using a multi-resonant circuit. The circuit consists of multiple microstrip U and L-shaped open stub resonators patterned in a unique configuration. The proposed configuration aids in capturing more data in a reduced space as well as tunable frequency operation. Tag and reader antennas utilize techniques such as stepped impedance feeding line, defective partial ground plane, and stair-step patch structure to achieve wide-band impedance bandwidth in miniature size. The results of the wireless measurements in the non-anechoic environment show that the proposed system has a reading range of more than 20 cm. The presented system possesses great potential for low-cost short-range inventory tracking.

RFID tag antennas with stable performance on the diverse electromagnetic mounting platforms are an integral part of the ubiquitous RFID systems. This research article presents a novel tag antenna design that facilitates the said objective. The proposed antenna consists of a modified H-shaped slot structure that ensures considerable robustness from the application environment through confining the surface current density within the antenna structure. The antenna offers a tunable bandwidth of 40 MHz within the microwave band of (2.4-2.5) GHz. The proposed tag antenna exhibits excellent response on metallic platforms of different sizes and thicknesses with an effective gain of almost four times of that in free space. Furthermore, the designed tag antenna performs adequately well on low-medium permittivity dielectrics (glass, paper, and plastic) and RF absorbers (water). The free space and on-metal performance of the proposed tag antenna are verified by testing a prototype realized on the FR4 substrate.

A novel, cedar-shaped, coplanar waveguide-fed frequency reconfigurable antenna is proposed. The presented antenna uses low-cost FR4 substrate with a thickness of 1.6 mm. Four PIN diodes are inserted on the antenna surface to variate the current distribution and alter the resonant frequencies with different combinations of switches. The proposed antenna is fabricated and measured for all states, and a good agreement is seen between measured and simulated results. This antenna resonates within the range of 2 GHz to 10 GHz, covering the major wireless applications of aviation service, wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX), long distance radio telecommunications, and X-band satellite communication. The proposed antenna works resourcefully with reasonable gain, significant bandwidth, directivity, and reflection coefficient. The proposed multiband reconfigurable antenna will pave the way for future wireless communications including WLAN, WiMAX, and possibly fifth-generation (5G) communication.

In this work a quad-band, planar, low-profile and compact antenna envisioned for incorporation into portable wireless devices is presented. The antenna is modeled on flexible Rogers RT/Duroid 5880 substrate of 0.127mm thickness. The proposed antenna structure consists of symmetrically placed F-shaped slits and a curved rectangular shaped ground plane with a CPW feed line. The four bands obtained for the radiator operates at the resonant frequencies 2.8, 3.9, 5.45 and 6.2 GHz with impedance bandwidths of 14%, 14.5%, 5.7%, and 5% respectively. Thus the proposed antenna supports WLAN, LTE, WiMAX, and C-band applications. The peak gain achieved for the antenna is 3.4 dB.

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

In this work, an integrated antenna system with Defected Ground Structure (DGS) is presented for Fourth Generation (4G) and millimeter (mm)-wave Fifth Generation (5G) wireless applications and handheld devices. The proposed design with overall dimensions of 110 mm x 75 mm is modeled on 0.508 mm thick Rogers RT/Duroid 5880 substrate. Radiating structure consists of antenna arrays excited by the T-shape 1 x 2 power divider/combiner. Dual bands for 4G centered at 3.8 GHz and 5.5 GHz are attained, whereas the 10-dB impedance bandwidth of 24.4 - 29.3 GHz is achieved for the 5G antenna array. In addition, a peak gain of 5.41 dBi is demonstrated across the operating bandwidth of the 4G antenna array. Similarly, for the 5G mm-wave configuration the attained peak gain is 10.29 dBi. Moreover, significant isolation is obtained between the two antenna modules ensuring efficient dual-frequency band operation using a single integrated solution. To endorse the concept, antenna prototype is fabricated and far-field measurements are procured. Simulated and measured results exhibit coherence. Also the proposed design is investigated for the beam steering capability of the mm-wave 5G antenna array using CST(R)MWS(R). The demonstrated structure offers various advantages including compactness, wide bandwidth, high gain, and planar configuration. Hence, the attained radiation characteristics prove the suitability of the proposed design for the current and future wireless handheld devices.

In this paper, we analyze the impact of buffer-aided full-duplex successive relay selection schemes with energy harvesting capability of relay nodes in amplifying and forward (AF) and decode and forward (DF) relaying environments for the Internet of Things networks. We propose to select a relay pair based on the energy harvested and signal strength at relay and destination to receive and transmit in the same time slot, respectively. Contrary to the previous relay pair selection schemes which are based on the signal strength only and cause the relay overuse problem, the proposed scheme ensures the balanced use of energy of relay nodes. The proposed relay selection scheme is implemented with the time switching (TS) and power splitting (PS)-based energy harvesting models in AF and DF relaying environments separately. Furthermore, we derive the closed-form expression of the outage probability and average throughput for both the TS and PS approaches in the DF and AF relaying modes. We compare the proposed relay selection scheme with the S-MMRS scheme and prove that the proposed scheme significantly reduces the outage probability and improves the average throughput. Furthermore, the analytical findings are reinforced with the extensive Monte Carlo simulations.

The development of autonomous vehicles or advanced driving assistance platforms has had a great leap forward to get closer to human daily life over the last decade. Nevertheless, it is still challenging to achieve an efficient and fully autonomous vehicle or driving assistance platform due to many strict requirements and complex situations or unknown environments. One of the main remaining challenges is a robust situation awareness in autonomous vehicles when the environment is unknoen. An autonomous system with a poor situation awareness due to low quantity or quality of data may directly or indirectly cause serious consequences. For instance, a person's life might be at risk due to a delay caused by a long or incorrect path planning of an autonomous ambulance. Internet of Everything (IoE) is currently becoming a prominent technology for many applications such as automation. In this paper, we propose an IoE-based architecture consisting of a heterogeneous team of cars and drones for enhancing situational awareness in autonomous cars, especially when dealing with critical cases of natural disasters. In particular, we show how an autonomous car can plan in advance the possible paths to a given destination, and send orders to other vehicles. These, in turn, perform terrain reconnaissance for avoiding obstacles and dealing with difficult situations. Together with a map merging algorithm deployed into the team autonomous vehicles, the proposed architecture can help to save traveling distance and time significantly in case of complex scenarios.

The iGrid project deals with the design and implementation of a solar-powered smart microgrid to supply electric power to small rural communities. In this paper, we discuss the roadmap of the iGrid project, which forms by merging the roadmaps of KIC (knowledge and Innovation Community) and CDE (Challenge-Driven Education). We introduce and explain a five-gear chain as Challenge, Education, Research, Innovation, and Deployment, called CERID, to reach the main goals of this project. We investigate the full chain in the iGrid project, which is established between KTH Royal Institute of Technology (Sweden) and University of Dar es Salam (Tanzania). We introduce the key stakeholders and explain how CERID goals can be accomplished in higher educations and through scientific research. Challenges are discussed, some innovative ideas are introduced and deployment solutions are recommended.

Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90% and an average recall over 95% in fall detection.