Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

Abstract

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.

False Data Filtering in Wireless Sensor Networks

“…Research from the current situation, filtering scheme based on the symmetric key is considered most suitable for WSN [11][12][13][14][15][16][17][18][19], and table 1 is more visually after comparing the pros and cons of common false data filtering scheme.…”

“…Although some scholars have been properly optimized, the cost is still too large, Therefore，These schemes need further research and optimization. Based on public key technology, it is the classic filtering schemes, location-based key [8][9], elliptic curve key [10] and LEDs mechanism [11], and so on. Because they can prevent collusion attacks, security better.…”

An Approach to Design and Implement RFID Middleware System over Cloud Computing

“…Problems include that computation cost is high, duplicate transmission cost is high, and inducing large delays when the total number of tags increase. EIFS [26] is claimed to have better performance than INPFM and CLIF regarding computational and communication cost; however, it can just eliminate about 80% of all redundancy, which may still be a problem for upper layer applications. Another issue for EIFS is that it just simulated a small number of tags (max 500 tags).…”

“…INPFM filters duplicated data at hop distance. As pointed out in [26], several problems still exist by applying INPFM or CLIF. Problems include that computation cost is high, duplicate transmission cost is high, and inducing large delays when the total number of tags increase.…”

“…Reference [25] introduces tuning approach in data filtering to reduce energy consumption in wireless sensor networks. Reference [26] presents an energy-efficient in-network RFID data filtering scheme in wireless sensor networks that has better performance regarding computational and communication costs than INPFM and CLIF. Tian et al [27] present a parallel method to redundancy data elimination in RFID networks by applying MapReduce and Hadoop cluster in Cloud computing.…”