Wireless Sensor Networks consists of sensor nodes which are placed in harsh and hostile surrounding where an adversary may capture the nodes, replicate them and use it for its own purpose. If the clone nodes remain undetected, it can disrupt the network functions making it vulnerable to attacks. Hence false data can be injected or…
Omnet++ Simulator
Multiple-Loop Self-Triggered Model Predictive Control for Network Scheduling and Control
We present an algorithm for controlling and scheduling multiple linear time-invariant processes on a shared bandwidth-limited communication network using adaptive sampling intervals. The controller is centralized and not only computes at every sampling instant the new control command for a process but also decides the time interval to wait until taking the next sample.The approach…
Priority-Driven Swapping-Based Scheduling of Aperiodic Real-Time Messages Over EtherCAT Networks
Real-time Ethernet (RTE) technologies are becoming increasingly popular, as they provide high bandwidth and are able to meet the requirements of industrial real-time communications. Among RTE protocols, the EtherCAT standard is suitable for motion control and closed-loop control applications, which require very short cycle times. As EtherCAT was specifically devised for periodic traffic, aperiodic real-time…
Sherlock Is Around: Detecting Network Failures with Local Evidence Fusion
Traditional approaches for wireless sensor network diagnosis are mainly sink-based. They actively collect global evidences from sensor nodes to the sink so as to conduct centralized analysis at the powerful back-end. On the one hand, long distance proactive information retrieval incurs huge transmission overhead; On the other hand, due to the coupling effect between diagnosis…
Bidirectional Buffer-Aided Relay Networks With Fixed Rate Transmission—Part I: Delay-Unconstrained Case
In this paper, we consider bidirectional relay networks in which two users exchange information only via a relay node, i.e., a direct link between both users is not present. We assume that channel state information at the transmitter is not available and/or only one coding and modulation scheme is used due to complexity constraints. Thus,…
A System-Level Cosynthesis Framework for Power Delivery and On-Chip Data Networks in Application-Specific 3-D ICs
With increasing core counts ushering in power-constrained 3-D multiprocessor system-on-chips (MPSoCs), optimizing communication power dissipated by the 3-D network-on-chip (NoC) fabric is critical. At the same time, with increased power densities in 3-D ICs, problems of IR drops in the power delivery network (PDN) as well as thermal hot spots on the 3-D die are…
Energy Efficiency Analysis of Cooperative Jamming in Cognitive Radio Networks with Secrecy Constraints
We investigate energy-efficient cooperation for se- crecy in cognitive radio networks. In particular, we consider a four-node cognitive scenario where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The cognitive transmitter should ensure that the primary message is not leaked to the secondary user by using cooperative jamming.…
Sensor Networks Localization: Extending Trilateration via Shadow Edges
Distance-based network localization is known to have solution, in general, if the network is globally rigid. In this paper we relax this condition with reference to unit disk graphs. To this end, shadow edges are introduced to model the fact that selected nodes are not able to sense each other. We provide a localization algorithm…
Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory
Service restoration is important in distribution networks following an outage. During the restoration process, the system operating conditions will fluctuate, including variation of the load demand and the output from distributed generators (DGs). These variations are hard to be predicted and the load demands are roughly estimated because of absence of real-time measurements, which can…
Multi-valued Neural Network Trained by Differential Evolution for Synthesizing Multiple-Valued Functions
We consider the problem of synthesizing multiple valued logic (MVL) functions by neural networks. A differential evolution algorithm is proposed to train the learnable multiple valued logic network. The optimum window and biasing parameters to be chosen for convergence are derived. Experiments performed on benchmark problems demonstrate the convergence and robustness of the network. Preliminary…