Nondeterministic space is closed under complementation
SIAM Journal on Computing
Turing Machines with Sublogarithmic Space
Turing Machines with Sublogarithmic Space
Space complexity of alternating Turing machines
FCT '85 Fundamentals of Computation Theory
Space hierarchy theorem revised
Theoretical Computer Science - Mathematical foundations of computer science
Stably computable predicates are semilinear
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Computation in networks of passively mobile finite-state sensors
Distributed Computing - Special issue: PODC 04
Self-stabilizing counting in mobile sensor networks
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
The Dynamics of Probabilistic Population Protocols
DISC '08 Proceedings of the 22nd international symposium on Distributed Computing
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Names Trump Malice: Tiny Mobile Agents Can Tolerate Byzantine Failures
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Recent Advances in Population Protocols
MFCS '09 Proceedings of the 34th International Symposium on Mathematical Foundations of Computer Science 2009
Hierarchies of memory limited computations
FOCS '65 Proceedings of the 6th Annual Symposium on Switching Circuit Theory and Logical Design (SWCT 1965)
Sensor Field: A Computational Model
Algorithmic Aspects of Wireless Sensor Networks
Not All Fair Probabilistic Schedulers Are Equivalent
OPODIS '09 Proceedings of the 13th International Conference on Principles of Distributed Systems
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
Stably decidable graph languages by mediated population protocols
SSS'10 Proceedings of the 12th international conference on Stabilization, safety, and security of distributed systems
Theoretical Computer Science
Theoretical Aspects of Distributed Computing in Sensor Networks
Theoretical Aspects of Distributed Computing in Sensor Networks
When birds die: making population protocols fault-tolerant
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Stably computable properties of network graphs
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Terminating population protocols via some minimal global knowledge assumptions
SSS'12 Proceedings of the 14th international conference on Stabilization, Safety, and Security of Distributed Systems
The computational power of simple protocols for self-awareness on graphs
Theoretical Computer Science
Causality, influence, and computation in possibly disconnected synchronous dynamic networks
Journal of Parallel and Distributed Computing
Hi-index | 5.23 |
We propose a new theoretical model for passively mobile wireless sensor networks, called PM, standing for passively mobile machines. The main modification w.r.t. the population protocol model (Angluin et al., 2006) [30] is that agents now, instead of being automata, are Turing Machines. We provide general definitions for unbounded memories, but we are mainly interested in computations upper-bounded by plausible space limitations. However, we prove that our results hold for more general cases. We focus on complete interaction graphs and define the complexity classes PMSPACE(f(n)) parametrically, consisting of all predicates that are stably computable by some PM protocol that uses O(f(n)) memory in each agent. We provide a protocol that generates unique identifiers from scratch only by using O(logn) memory, and use it to provide an exact characterization of the classes PMSPACE(f(n)) when f(n)=@W(logn): they are precisely the classes of all symmetric predicates inNSPACE(nf(n)). As a consequence, we obtain a space hierarchy of the PM model when the memory bounds are @W(logn). We next explore the computability of the PM model when the protocols use o(loglogn) space per machine and prove that SEM=PMSPACE(f(n)) when f(n)=o(loglogn), where SEM denotes the class of the semilinear predicates. Finally, we establish that the minimal space requirement for the computation of non-semilinear predicates is O(loglogn).