Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.

Up to date energy-aware radio frequency identification (RFID) anticollision protocols are mainly focused on RFID systems using active tags. The introduction of RFID in portable devices, and the increasing popularity of passive tags, demands low energy-consumption anticollision protocols in environments with passive tags. Memoryless protocols present the simplest tag hardware that is beneficial for a low energy consumption. However, the excessive collisions required by these protocols increases the energy consumption, wasting a large number of tag transmitting bits. In this paper, a window procedure is presented to manage the length of the tags' bit-stream in order to limit the energy wastage in collisions. The bit window is applied to two memoryless anticollision protocols, the query tree protocol (QT) and the collision tree protocol (CT), producing the query window tree protocol (QwT) and the collision window tree protocol (CwT). Simulations show that the use of this window yields significant reductions in energy consumption and identification time. In addition, QwT and CwT are compared to state of the art memoryless protocols to confirm that CwT is an outstanding candidate for reducing energy consumption. Also, the results obtained show how the bit window procedure increases energy savings as the length of the tag ID increases.

This paper describes a telematic system based on an intelligent van which is capable of tracing pharmaceutical drugs over delivery routes from a warehouse to pharmacies, without altering carriers’ daily conventional tasks. The intelligent van understands its environment, taking into account its location, the assets and the predefined delivery route; with the capability of reporting incidences to carriers in case of failure according to the established distribution plan. It is a non-intrusive solution which represents a successful experience of using smart environments and an optimized Radio Frequency Identification (RFID) embedded system in a viable way to resolve a real industrial need in the pharmaceutical industry. The combination of deterministic modeling of the indoor vehicle, the implementation of an ad-hoc radiating element and an agile software platform within an overall system architecture leads to a competitive, flexible and scalable solution.

The knowledge of the number of tags is critical in many radio frequency identification (RFID) applications. This paper is concerned with the problem of estimating an RFID tag population size when the number of tags is much higher than the frame size. A novel estimation scheme called “Scalable Minimum Mean Square Error” (sMMSE) is proposed. The proposed estimator updates the frame size by considering two key parameters: One determines the limit of the slots occupancy at which the frame size should be increased, and the other one sets the frame size increase factor. A formal study is provided to adjust these parameters with the aim of lowering the estimation error while scaling to highly populated tag sets. Numerical results indicate that sMMSE significantly decreases the normalized estimation error and maintains a low estimation time compared to existing strategies.

Radio Frequency Identification (RFID) is a technology that uses radio frequency signals to identify objects. RFID is one of the key technologies used by the Internet of Things (IoT). This technology enables communication between the main devices used in RFID, the reader and the tags. The tags share a communication channel. Therefore, if several tags attempt to send information at the same time, the reader will be unable to distinguish these signals. This is called the tag collision problem. This results in an increased time necessary for system identification and energy consumption. To minimize tag collisions, RFID readers must use an anti-collision protocol. Different types of anti-collision protocols have been proposed in the literature in order to solve this problem. This paper provides an update including some of the most relevant anti-collision protocols.