In the near future, multi-processor systems-on-chip (MPSoC) will become the main thrust driving the evolution of integrated circuits. MPSoCs introduce new challenges, mainly due to growing communication through their interconnect structure. Current electrical interconnects face hard challenges to overcome such data flows. Integrated optical interconnect is a potential technological improvement to reduce these problems. The main contributions of this paper are: i) the optical network integration in a system-level MPSoC platform; and ii) the quantitative evaluation of optical interconnect for MPSoC design using a multimedia application

This paper presents a heterogeneous model of an optical network-on chip (ONoC). An ONoC is an optical interconnect architecture integrated on a system-on-chip, and is intended to replace traditional electrical networks-on-chip (NoC) to overcome their future bandwidth limitations. To evaluate the advantages of a technological implementation of an ONoC, it is necessary to model its behavior and to realize a virtual prototype to estimate its power, latency, area, bandwidth, and subsequently to compare these parameters with the performance of a classical NoC. To model the ONoC at a high abstraction level, a rich system-level design language is used (SystemC). A bottom-up approach is used for the high level ONoC model description, and the performance values used at this level are extracted from the physical level with specific tools and models.

Energy consumption is one of the most important metrics in wireless sensor networks because of the limited power supply in sensor nodes. Many efforts have been taken to reduce the energy consumption of the hardware, software, communication protocols and applications. Simulation is widely used to evaluate the performance of these new designs. Thus, it is necessary to accurately model the energy consumption during the simulation of WSN. In this paper, an energy model for WSN is proposed. It has been implemented in IDEA1, a simulator for WSN developed by SystemC and C++. It enables the energy estimation of both the hardware components of an individual node and the whole sensor network. It can be easily calibrated to different types of node if the electrical characteristics of the hardware components are available. An application based on IEEE 802.15.4 and MICAz motes is studied to demonstrate the capability of the energy model.

Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network’s lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN’s lifetime. By categorizing existing strategies into node energy reduction, network energy balancing, and energy replenishment, this study assesses their effectiveness when implemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems.