Abstract We propose a novel framework that combines probabilistic transmission with Latin Squares characteristics to tune channel access, meeting various demands in network performance (Energy vs. Delay). The proposed technique is decentralized, scalable, and has low overhead. We develop an analytical model to estimate the network performance and validate the benefits of the proposed framework via simulation-based experiments. Keywords: adaptable access, sensor network, network performance I. INTRODUCTION mart sensor networks naturally apply to a broad range of applications with different requirements to network performance. One of the major requirements is proper energy utilization in Wireless Sensor Networks (WSN) [5] [9]. At the same time, minimizing sensor query response time is equally crucial in mission-critical sensor networks. In general, the time/energy trade-offs involve energy and time gains/losses associated with specific channel access methods. In this paper we consider the network performance issue in WSNs from a different perspective. Instead of designing an explicit communication protocol to meet certain objectives, we propose tuning the channel access mechanism in an adaptable way to handle different application requirements. In this paper we emphasize performance tuning with objectives to minimize Energy consumption and Delay. Maintaining acceptable query response time and high energy efficiency is a Multi-objective Optimization Problem (MOP) [2]. In general, MOP aims at minimizing the values of several objective functions f1 … fn under a given set of constraints. In most cases it is unlikely that different objectives would be optimized by the same choice of parameters (i.e., vectors). To choose between different vectors of the optimization objectives, an optimizer utilizes the concept of Pareto optimality [2]. Informally, an objective vector is said to be Pareto optimal if all other feasible vectors in the objective space have a higher value for at least one of the objective functions, or else have the same value for all objectives. Typically, there is more than one Pareto optimal vector (Pareto points) reflecting the trade-offs between different objectives. Our adaptable transmission approach follows the concept of Pareto optimality while considering the tradeoffs between energy and delay objectives. We propose a novel access scheme, Adaptable Probabilistic Transmission framework (APT). APT is an extension of Cyclic Probabilistic Transmission protocol (CPT) [3] that performs data transmissions based on predefined probabilities. In a manner similar to CPT, APT maintains a Transmission Probability Matrix (TPM), each cell of which holds a probability of data transmission of a sensor within a given time slot. The TPM specifies one “network transmission cycle” where every sensor knows its own transmission probabilities in fixed time slots and repetitively uses the TPM for distributed data transmissions. In addition, location information is used to improve channel access. Similar to the Grid-based Latin Squares Scheduling Access Protocol (GLASS) [11], sensors divide the monitoring area into a virtual grid. Then each sensor associates itself with one virtual grid cell or sector using geographical data. This design allows neighboring sensors to maintain spatial and temporal separation between potentially colliding packets while keeping channel access scalable. Note that APT assumes that each node in a WSN is aware of its geographic location 1 . While location-based approaches have been adopted in routing mechanisms [1], to the best of our knowledge they are rarely utilized for optimizing channel access. Finally, APT uses the Latin Squares characteristics (LS) [4] to tune the probabilities in the TPMs of sensors enabling trade-offs between our optimization objectives. We demonstrate feasibility of our technique analytically and using simulations. The analytical model captures the characteristics of the WSN with APT while the simulation study validates the analysis and the performance in different networking environments. In particular, the simulation results show that the APT control overhead cost is very low since APT is based only on local sensor processing. The organization of this paper is as follows: in Section II we discuss related work on channel access of WSNs. In Section III we introduce the new APT framework. In Section IV we briefly summarize the APT analysis and in Section V we test its feasibility via simulations. Finally, in Section VI we offer our conclusion. II. RELATED WORK Channel access in WSNs can be classified into scheduling- based and random-based access categories. Random-based 1 We note that using global positioning system (GPS) is not always possible in WSNs because of energy and location precision constraints. WSNs commonly utilize ad hoc localization methods based on nodes, calculating their coordinates using special beacon nodes whose positions are known. Further consideration of this subject is beyond of the scope of this paper. Adaptable Probabilistic Transmission Framework for Wireless Sensor Networks Chih-Kuang Lin Vladimir Zadorozhny and Prashant Krishnamurthy Q2S, NTNU, Norway University of Pittsburgh, USA chih@q2s.ntnu.no {vladimir, prashant}@sis.pitt.edu S 2009 Third International Conference on Sensor Technologies and Applications 978-0-7695-3669-9/09 $25.00 © 2009 IEEE DOI 10.1109/SENSORCOMM.2009.109 676