Perimeter-Based Data Replication in Mobile Sensor Networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme  that enables the  exact retrieval of events from the network in cases of failures. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures are less than 60% failure then we can recover over 80% of generated events exactly.

[1]  Gaurav S. Sukhatme,et al.  Robomote: enabling mobility in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[2]  Panos K. Chrysanthis,et al.  MINT Views: Materialized In-Network Top-k Views in Sensor Networks , 2007, 2007 International Conference on Mobile Data Management.

[3]  Ramesh Govindan,et al.  Localized edge detection in sensor fields , 2003, Ad Hoc Networks.

[4]  Dimitrios Gunopulos,et al.  Microhash: an efficient index structure for fash-based sensor devices , 2005, FAST'05.

[5]  John Anderson,et al.  An analysis of a large scale habitat monitoring application , 2004, SenSys '04.

[6]  Kirk Pruhs,et al.  KDDCS: a load-balanced in-network data-centric storage scheme for sensor networks , 2006, CIKM '06.

[7]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

[8]  Krithi Ramamritham,et al.  ACE in the Hole: Adaptive Contour Estimation Using Collaborating Mobile Sensors , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[9]  Wei Hong,et al.  The design of an acquisitional query processor for sensor networks , 2003, SIGMOD '03.

[10]  Yong Yao,et al.  The cougar approach to in-network query processing in sensor networks , 2002, SGMD.

[11]  Kamran Mohseni,et al.  SensorFlock: an airborne wireless sensor network of micro-air vehicles , 2007, SenSys '07.

[12]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[13]  Kristofer S. J. Pister,et al.  CotsBots: an off-the-shelf platform for distributed robotics , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[14]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[15]  Panos K. Chrysanthis,et al.  SenseSwarm: a perimeter-based data acquisition framework for mobile sensor networks , 2007, DMSN '07.

[16]  Sushil Jajodia,et al.  Dynamic voting algorithms for maintaining the consistency of a replicated database , 1990, TODS.

[17]  Agathoniki Trigoni,et al.  A drift-tolerant model for data management in ocean sensor networks , 2007, MobiDE '07.

[18]  A. Singh,et al.  Fault-tolerant systems , 1990, Computer.

[19]  Deborah Estrin,et al.  Data-centric storage in sensornets , 2003, CCRV.

[20]  Christiaan J. J. Paredis,et al.  Millibots: The Development of a Framework and Algorithms for a Distributed Heterogeneous Robot Team , 2002 .

[21]  Ryan Newton,et al.  The pothole patrol: using a mobile sensor network for road surface monitoring , 2008, MobiSys '08.

[22]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[23]  Margaret Martonosi,et al.  Hardware design experiences in ZebraNet , 2004, SenSys '04.

[24]  Robert Szewczyk,et al.  System architecture directions for networked sensors , 2000, ASPLOS IX.

[25]  Philip Levis,et al.  TINX: a tiny index design for flash memory on wireless sensor devices , 2006, SenSys '06.

[26]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[27]  Panos K. Chrysanthis,et al.  Mobile Sensor Network Data Management , 2009, Encyclopedia of Database Systems.