Batch conflict resolution algorithm with progressively accurate multiplicity estimation

Abstract

The wireless connectivity, essential for pervasive computing, has ephemeral character and can be used for creating ad hoc networks, sensor networks, connection with RFID tags etc. The communication tasks in such wireless networks often involve an inquiry over a shared channel, which can be invoked for: discovery of neighboring devices in ad hoc networks, counting the number of active sensors in sensor networks, estimating the mean value contained in a group of sensors etc. Such inquiry solicits replies from possibly large number of terminals n. This necessitates the usage of algorithms for resolving batch conflicts with unknown conflict multiplicity n. In this paper we present a novel approach to the batch conflict resolution. We show how the conventional tree algorithms for conflict resolution can be used to obtain progressively accurate estimation of the multiplicity. We use the estimation to propose a more efficient binary tree algorithm, termed Estimating Binary Tree (EBT) algorithm. We extend the approach to design the Interval Estimation Conflict Resolution (IECR) algorithm. For n → ∞ we prove that the efficiency achieved by IECR for batch arrivals is identical with the efficiency that Gallager's FCFS algorithm achieves for Poisson packet arrivals. For finite n, the simulation results show that IECR is, to the best of our knowledge, the most efficient batch resolution algorithm reported to date.

Fast and reliable estimation schemes in RFID systems

“…These schemes assume that each tag can understand and respond to complex commands from the reader, such as responding only if the ID is within an address range specified by the reader. Examples of such algorithms can be found in [21,26,27,37,38,39,40,43,44,29,30]. While these algorithms deterministically resolve conflicts, they often discount the amount of time that readers spend in probing and isolating different segments of the tag population in computing the total time taken to resolve all conflicts.…”

“…These algorithms fall into the class of tree-based identification algorithms [21,26,27,37,38,39,40,43,44,29,30] with the tags classified on a binary tree based on their id, and the reader moving down the tree at each step to identify all nodes. Deterministic algorithms are typically faster than probabilistic schemes in terms of actual tag response slots used, however, they suffer from large reader overhead since the reader has to specify address ranges to isolate contending tag subsets using a probe at the beginning of each slot.…”

A Channel Aware Contention Based Forwarding Scheme in Wireless Sensor Network

“…In our scheme, token j t can be regarded as the access probability of node j without considering the channel condition or routing metrics [10]. To increase the fairness of access and to balance the energy of the network, we tend to choose node with lesser token as the relay.…”

Integrated Cost-Based MAC and Routing Techniques for Hop Count Forwarding in Wireless Sensor Networks

“…7 Note that N and E can be estimated at every node. Sender initiated algorithms for the estimation of N can be found in [30]. For E, one can low pass filter the minimum costs measured at every forwarding step.…”