Secure K-Nearest neighbor queries in two-tiered mobile wireless sensor networks

Tiered Mobile Wireless Sensor Network(TMWSN)is a new paradigm introduced by mobile edge computing.Now it has received wide attention because of its high scalability,robustness,deployment flexibility,and it has a wide range of application scenarios.In TMWSNs,the storage nodes are the key nodes of the network and are more easily captured and utilized by attackers.Once the storage nodes are captured by the attackers,the data stored on them will be exposed.Moreover,the query process and results will not be trusted any more.This paper mainly studies the secure KNN query technology in TMWSNs,and we propose a secure KNN query algorithm named the Basic Algorithm For Secure KNN Query(BAFSKQ)first,which can protect privacy and verify the integrity of query results.However,this algorithm has a large communication overhead in most cases.In order to solve this problem,we propose an improved algorithm named the Secure KNN Query Algorithm Based on MR-Tree(SEKQAM).The MR-Trees are used to find the K-nearest locations and help to generate a verification set to process the verification of query results.It can be proved that our algorithms can effectively guarantee the privacy of the data stored on the storage nodes and the integrity of the query results.Our experimental results also show that after introducing the MR-Trees in KNN queries on TMWSNs,the communication overhead has an effective reduction compared to BAFSKQ.

HilAnchor:Location Privacy Protection in the Presence of Users' Preferences

Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users＇ preferences or incompletely fulfill privacy preferences.In this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred region.Via Hilbert curve based transformation,the additional workload from users＇ preferences is alleviated.Furthermore,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D space.Therefore,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert curve.Further,details of choosing anchor points are theoretically elaborated.The empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.

Monitoring continuous k-nearest neighbor queries in the hybrid wireless network

In a mobile/pervasive computing environment,one of the most important goals of monitoring continuous spatial queries is to reduce communication cost for location-updates.Existing work uses many cellular wireless connections,which would easily become the performance bottleneck of the overall system.This paper introduces a novel continuous kNN query monitoring method to reduce communication cost in the hybrid wireless network,where the moving objects in the wireless broadcasting system construct the ad-hoc network.Simulation results prove the efficiency of the proposed method,which leverages the wireless broadcasting channel as well as the WiFi link to alleviate the burden on the cellular uplink communication cost.