Energy efficient anti-collision algorithm for the RFID networks

Energy efficiency is crucial for radio frequency identification (RFID) systems as the readers are often battery operated. The main source of the energy wastage is the collision which happens when tags access the communication medium at the same time. Thus, an efficient anti-collision protocol could minimize the energy wastage and prolong the lifetime of the RFID systems. In this regard, EPCGlobal-Class1-Generation2 (EPC-C1G2) protocol is currently being used in the commercial RFID readers to provide fast tag identification through efficient collision arbitration using the Q algorithm. However, this protocol requires a lot of control message overheads for its operation. Thus, a reinforcement learning based anti-collision protocol (RL-DFSA) is proposed to provide better time system efficiency while being energy efficient through the minimization of control message overheads. The proposed RL-DFSA was evaluated through extensive simulations and compared with the variants of EPC-Class 1 Generation 2 algorithms that are currently being used in the commercial readers. The results show conclusively that the proposed RL-DFSA performs identically to the very efficient EPC-C1G2 protocol in terms of time system efficiency but readily outperforms the compared protocol in the number of control message overhead required for the operation

Energy efficient routing protocols for wireless sensor networks: comparison and future directions

Wireless sensor network consists of nodes with limited resources. Hence, it is important to design protocols or algorithms which increases energy efficiency in order to improve the network lifetime. In this paper, techniques used in the network layer (routing) of the internet protocol stack to achieve energy efficiency are reviewed. Usually, the routing protocols are classified into four main schemes: (1) Network Structure, (2) Communication Model, (3) Topology Based, and (4) Reliable Routing. In this work, only network structure based routing protocols are reviewed due to the page constraint. Besides, this type of protocols are much popular among the researchers since they are fairly simple to implement and produce good results as presented in this paper. Also, the pros and cons of each protocols are presented. Finally, the paper concludes with possible further research directions

Energy efficient routing protocols for wireless sensor networks: comparison and future directions

Wireless sensor network consists of nodes with limited resources. Hence, it is important to design protocols or algorithms which increases energy efficiency in order to improve the network lifetime. In this paper, techniques used in the network layer (routing) of the internet protocol stack to achieve energy efficiency are reviewed. Usually, the routing protocols are classified into four main schemes: (1) Network Structure, (2) Communication Model, (3) Topology Based, and (4) Reliable Routing. In this work, only network structure based routing protocols are reviewed due to the page constraint. Besides, this type of protocols are much popular among the researchers since they are fairly simple to implement and produce good results as presented in this paper. Also, the pros and cons of each protocols are presented. Finally, the paper concludes with possible further research directions

A fuzzy-based angle-of-arrival estimation system (AES) using radiation pattern reconfigurable (RPR) antenna and modified gaussian membership function

Angle-of-arrival (AOA) estimation is an important factor in various wireless sensing applications, especially localization systems. This paper proposes a new type of AOA estimation sensor node, known as AOA-estimation system (AES) where the received signal strength indication (RSSI) from multiple radiation pattern reconfigurable (RPR) antennas are used to calculate the AOA. In the proposed framework, three sets of RPR antennas have been used to provide a coverage of 15 regions of radiation patterns at different angles. The salient feature of this RPR-based AOA estimation is the use of Fuzzy Inferences System (FIS) to further enhance the number of estimation points. The introduction of a modified FIS membership function (MF) based on Gaussian function resulted in an improved 85% FIS aggregation percentage between the fuzzy input and output. This later resulted in a low AOA error (of less than 5%) and root-mean-square error (of less than 8°)