A Bit-Tracking Knowledge-Based Query Tree for RFID Tag Identification in IoT Systems.

In an IoT (Internet of Things) system where each IoT device has one/many RFID tags, there might be many RFID tags. However, when multiple tags respond to the reader's interrogation at the same time, their signals collide. Due to the collision, the reader must request the colliding tags to retransmit their IDs, resulting in higher communication overhead and longer identification time. Therefore, this paper presents a Bit-tracking Knowledge-based Query Tree (BKQT), which uses two techniques: knowledge, which stores all the tag IDs that can possibly occur, and bit tracking, which allows the reader to detect the locations of the collided bits in a collision slot. BKQT constructs a query tree for all possible tags, called a k-tree, by using knowledge while it constructs bit-collision cases and the corresponding actions for each node in this k-tree by using bit tracking. In the identification process, BKQT traverses this constructed k-tree and thus identifies the colliding tags faster by taking the actions according to the happening bit-collision cases. From the simulation results, BKQT can improve the identification time by 44.3%, 46.4%, and 25.1%, compared with the previous knowledge-based protocols, Knowledge Query Tree (KQT), Heuristic Query Tree (H-QT), Query Tree with Shortcutting and Couple Resolution (QTSC), respectively.

Social-Aware Peer Discovery for Energy Harvesting-Based Device-to-Device Communications.

In Device-to-Device (D2D) communications, the first step is to find all of the neighboring peers in the network by performing a peer discovery process. Most previous studies use the social behaviors of the users to adjust the sending rates of the peer discovery messages (i.e., beacons) under the constraint of consumed power for increasing the Peer Discovery Ratio (PDR). However, these studies do not consider the potential for energy harvesting, which allows for the User Equipments (UEs) to procure additional power within charging areas. Accordingly, this paper proposes an Energy-Ratio Rate Decision (ERRD) algorithm that comprises three steps, namely Social Ratio Allocation (SRA), Energy Ratio Allocation (ERA), and Beacon Rate Decision (BRD). The SRA step determines the allocated power quantum for each UE from the total budget power based on the social behavior of the UE. The ERA step then adjusts this allocated power quantum in accordance with the power that is harvested by the UE. Finally, the BRD step computes the beacon rate for the UE based on the adjusted power quantum. The simulation results show that ERRD outperforms the previously-reported Social-Based Grouping (SBG) algorithm by 190% on the PDR for a budget power of one watt and 8% for a budget power of 20 watts.