2,202 research outputs found
Random Broadcast Based Distributed Consensus Clock Synchronization for Mobile Networks
Clock synchronization is a crucial issue for mobile ad hoc networks due to the dynamic and distributed nature of these networks. In this paper, employing affine models for local clocks, a random broadcast based distributed consensus clock synchronization algorithm is proposed. In the absence of transmission delays, we theoretically prove the convergence of the proposed scheme, which is further illustrated by numerical results. In addition, it is concluded from simulations that the proposed scheme is scalable and robust to transmission delays as well as different accuracy requirements
Adaptive Synchronization of Robotic Sensor Networks
The main focus of recent time synchronization research is developing
power-efficient synchronization methods that meet pre-defined accuracy
requirements. However, an aspect that has been often overlooked is the high
dynamics of the network topology due to the mobility of the nodes. Employing
existing flooding-based and peer-to-peer synchronization methods, are networked
robots still be able to adapt themselves and self-adjust their logical clocks
under mobile network dynamics? In this paper, we present the application and
the evaluation of the existing synchronization methods on robotic sensor
networks. We show through simulations that Adaptive Value Tracking
synchronization is robust and efficient under mobility. Hence, deducing the
time synchronization problem in robotic sensor networks into a dynamic value
searching problem is preferable to existing synchronization methods in the
literature.Comment: First International Workshop on Robotic Sensor Networks part of
Cyber-Physical Systems Week, Berlin, Germany, 14 April 201
Task allocation in group of nodes in the IoT: A consensus approach
The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach
PADS: Practical Attestation for Highly Dynamic Swarm Topologies
Remote attestation protocols are widely used to detect device configuration
(e.g., software and/or data) compromise in Internet of Things (IoT) scenarios.
Unfortunately, the performances of such protocols are unsatisfactory when
dealing with thousands of smart devices. Recently, researchers are focusing on
addressing this limitation. The approach is to run attestation in a collective
way, with the goal of reducing computation and communication. Despite these
advances, current solutions for attestation are still unsatisfactory because of
their complex management and strict assumptions concerning the topology (e.g.,
being time invariant or maintaining a fixed topology). In this paper, we
propose PADS, a secure, efficient, and practical protocol for attesting
potentially large networks of smart devices with unstructured or dynamic
topologies. PADS builds upon the recent concept of non-interactive attestation,
by reducing the collective attestation problem into a minimum consensus one. We
compare PADS with a state-of-the art collective attestation protocol and
validate it by using realistic simulations that show practicality and
efficiency. The results confirm the suitability of PADS for low-end devices,
and highly unstructured networks.Comment: Submitted to ESORICS 201
Distributed Local Linear Parameter Estimation using Gaussian SPAWN
We consider the problem of estimating local sensor parameters, where the
local parameters and sensor observations are related through linear stochastic
models. Sensors exchange messages and cooperate with each other to estimate
their own local parameters iteratively. We study the Gaussian Sum-Product
Algorithm over a Wireless Network (gSPAWN) procedure, which is based on belief
propagation, but uses fixed size broadcast messages at each sensor instead.
Compared with the popular diffusion strategies for performing network parameter
estimation, whose communication cost at each sensor increases with increasing
network density, the gSPAWN algorithm allows sensors to broadcast a message
whose size does not depend on the network size or density, making it more
suitable for applications in wireless sensor networks. We show that the gSPAWN
algorithm converges in mean and has mean-square stability under some technical
sufficient conditions, and we describe an application of the gSPAWN algorithm
to a network localization problem in non-line-of-sight environments. Numerical
results suggest that gSPAWN converges much faster in general than the diffusion
method, and has lower communication costs, with comparable root mean square
errors
Cooperative Synchronization in Wireless Networks
Synchronization is a key functionality in wireless network, enabling a wide
variety of services. We consider a Bayesian inference framework whereby network
nodes can achieve phase and skew synchronization in a fully distributed way. In
particular, under the assumption of Gaussian measurement noise, we derive two
message passing methods (belief propagation and mean field), analyze their
convergence behavior, and perform a qualitative and quantitative comparison
with a number of competing algorithms. We also show that both methods can be
applied in networks with and without master nodes. Our performance results are
complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds
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