Abstract: We here present the Forward Planning Situated Protocol (FPSP), for scalable, energy efficient and fault tolerant data propagation in situated wireless sensor networks. To deal with the increased complexity of such deeply networked sensor systems, instead of emphasizing on a particular aspect of the services provided, i.e. either for low-energy periodic, or low-latency event-driven, or high-success query-based sensing, FPSP uses two novel mechanisms that allow the network operator to adjust the performance of the protocol in terms of energy, latency and success rate on a per-task basis. We emphasize on distributedness, direct or indirect interactions among relatively simple agents, flexibility and robustness.
The protocol operates by employing a series of plan & forward phases through which devices self-organize into forwarding groups that propagate data over discovered paths. FPSP performs a limited number of long range, high power data transmissions to collect information regarding the neighboring devices. The acquired information, allows to plan a (parameterizable long by {\"e}) sequence of short range, low power transmissions between nearby particles, based on certain optimization criteria. All particles that decide to respond (based on local criteria) to these long range transmissions enter the forwarding phase during which information is propagated via the acquired plan. Clearly, the duration of the forwarding phases is characterized by the parameter {\"e}, the transmission medium and the processing speed of the devices. In fact the parameter {\"e} provides a mechanism to adjust the protocol performance in terms of the latency--energy trade-off. By reducing {\"e} the latency is reduced at the cost of spending extra energy, while by increasing {\"e}, the energy dissipation is reduced but the latency is increased.
To control the success rate--energy trade-off, particles react locally on environment and context changes by using a set of rules that are based on response thresholds that relate individual-level plasticity with network-level resiliency, motivated by the nature-inspired method for dividing labor, a metaphor of social insect behavior for solving problems [1]. Each particle has an individual response threshold {\`E} that is related to the "local" density (as observed by the particle, [2]); particles engage in propagation of events when the level of the task-associated stimuli exceeds their thresholds. Let s be the intensity of a stimulus associated with a particular sensing task, set by the human authorities. We adopt the response function T{\`e}(s) = snover sn + {\`e}n, the probability of performing the task as a function of s, where n > 1 determines the steepness of the threshold. Thus, when {\`e} is small (i.e. the network is sparse) then the response probability increases; when s increases (i.e. for critical sensing tasks) the response probability increases as well.
This role-based approach where a selective number of devices do the high cost planning and the rest of the network operates in a low cost state leads to systems that have increased energy efficiency and high fault-tolerance since these long range planning phases allow to bypass obstacles (where no sensors are available) or faulty sensors (that have been disabled due to power failure or other natural events).
Abstract: The problem of robust line planning requests for a set of
origin-destination paths (lines) along with their frequencies in an underlying
railway network infrastructure, which are robust to
uctuations of
real-time parameters of the solution.
In this work, we investigate a variant of robust line planning stemming
from recent regulations in the railway sector that introduce competition
and free railway markets, and set up a new application scenario: there is
a (potentially large) number of line operators that have their lines xed
and operate as competing entities struggling to exploit the underlying
network infrastructure via frequency requests, while the management of
the infrastructure itself remains the responsibility of a single (typically
governmental) entity, the network operator.
The line operators are typically unwilling to reveal their true incentives.
Nevertheless, the network operator would like to ensure a fair (or, socially
optimal) usage of the infrastructure, e.g., by maximizing the (unknown to
him) aggregate incentives of the line operators. We show that this can be
accomplished in certain situations via a (possibly anonymous) incentivecompatible
pricing scheme for the usage of the shared resources, that is
robust against the unknown incentives and the changes in the demands
of the entities. This brings up a new notion of robustness, which we
call incentive-compatible robustness, that considers as robustness of the
system its tolerance to the entities' unknown incentives and elasticity
of demands, aiming at an eventual stabilization to an equilibrium point
that is as close as possible to the social optimum.
Abstract: We consider a fundamental problem, called QoS-aware Multicommodity Flow, for assessing robustness in transportation planning.
It constitutes a natural generalization of the weighted multicommodity
flow problem, where the demands and commodity values are elastic to
the Quality-of-Service (QoS) characteristics of the underlying network.
The problem is also fundamental in other domains beyond transportation
planning. In this work, we provide an extensive experimental study of
two FPTAS for the QoS-aware Multicommodity Flow Problem enhanced
with several heuristics, and show the superiority of a new heuristic we
introduce here.
Abstract: We consider the line planning problem in public transporta-
tion, under a robustness perspective. We present a mechanism for robust
line planning in the case of multiple line pools, when the line operators
have a different utility function per pool. We conduct an experimen-
tal study of our mechanism on both synthetic and real-world data that
shows fast convergence to the optimum. We also explore a wide range of
scenarios, varying from an arbitrary initial state (to be solved) to small
disruptions in a previously optimal solution (to be recovered). Our ex-
periments with the latter scenario show that our mechanism can be used
as an online recovery scheme causing the system to re-converge to its
optimum extremely fast.
Abstract: The problem of robust line planning requests for a set of
origin-destination paths (lines) along with their tra±c rates (frequencies)
in an underlying railway network infrastructure, which are robust to
°uctuations of real-time parameters of the solution.
In this work, we investigate a variant of robust line planning stemming
from recent regulations in the railway sector that introduce competition
and free railway markets, and set up a new application scenario: there is
a (potentially large) number of line operators that have their lines ¯xed
and operate as competing entities struggling to exploit the underlying
network infrastructure via frequency requests, while the management of
the infrastructure itself remains the responsibility of a single (typically
governmental) entity, the network operator.
The line operators are typically unwilling to reveal their true incentives.
Nevertheless, the network operator would like to ensure a fair (or, socially
optimal) usage of the infrastructure, e.g., by maximizing the (unknown to
him) aggregate incentives of the line operators. We show that this can be
accomplished in certain situations via a (possibly anonymous) incentive-
compatible pricing scheme for the usage of the shared resources, that is
robust against the unknown incentives and the changes in the demands
of the entities. This brings up a new notion of robustness, which we
call incentive-compatible robustness, that considers as robustness of the
system its tolerance to the entities' unknown incentives and elasticity
of demands, aiming at an eventual stabilization to an equilibrium point
that is as close as possible to the social optimum.
