Proposition and validation of an original MAC layer with simultaneous medium accesses for low latency wireless control/command applications

Control/command processes require a transmission system with some characteristics like high reliability, low latency and strong guarantees on messages delivery. Concerning wire networks, field buses technologies like FIP offer this kind of service (periodic tasks, real time constraints...). Unfortunately, few wireless technologies can propose a communication system which respects such constraints. Indeed, wireless transmissions must deal with medium characteristics which make impossible the direct translation of mechanisms used with wire networks. The purpose of this paper is to present an original Medium Access Control (MAC) layer for a real time Low Power-Wireless Personal Area Network (LP-WPAN). The proposed MAC-layer has been validated by several complementary methods; in this paper, we focus on the specific Simultaneous Guaranteed Time Slot (SGTS) part

Internet of Things Applications in Precision Agriculture: A Review

The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases

CCRP: A Novel Clone-Based Cloud Robotic Platform for Multi-Robots

Recently, the cloud computing paradigm has evolved from various research fields. A new path of research, cloud robotics, has emerged which allows robots to inherit the enormous computing and storage capability of cloud. Advances in cloud computing technologies, networking, parallel computing and other evolving technologies, and the integration with multi-robot systems, make it possible to design systems with new capabilities. The main advantages of cloud robotics are in overcoming the limitations of on-board robot computing and storage capabilities and in improving energy efficiency. Nevertheless, there is a lack of cloud robotics frameworks that can provide a secured environment for multi-robot application. The implementation of a robust cloud robotic platform capable of handling multi-robot applications has been shown to be challenging. This research proposes a novel Clone-based Cloud Robotic Platform architecture (CCRP) which assigns a Virtual Machine (VM) clone of each individual robot's operating system in the cloud, enabling fast and efficient collaboration between them via the cloud's inner-network. The system utilises Robot Operating System (ROS) as a middleware and programmable environment for robot development. This model is using the OpenVPN as a communication protocol between the robot and the VM, which provides considerable enhancement for the security and additional network for the system to allow multi-master ROS deployment. The Quality of Service (QoS) for the system has been tested and evaluated in terms of performance, compatibility and scalability via comparison study, which examines the CCRP performance against a local system and a proxy-based cloud system. Two case studies have been deployed for different robot scenarios. Case study 1 was focused on a navigation task which includes the process of mapping and teleoperation implemented in Google public cloud. The real time response has been examined by using the CCRP to teleoperate the NAO and Turtlebot robots. A response time and video streaming delays were measured to assess the overall QoS performance. Case study 2 is composed of a face recognition task performed using the CCRP in a private cloud on an Openstack platform. The objective of this task was to evaluate the system ability of running the tasks in the cloud effectively and to assess the collaborative learning capability. During the CCRP development and deployment stages an optimization study was conducted to determine optimal parameters for data offloading to the cloud and energy efficiency of a low-cost robot. The result of the CCRP performance evaluation proved that it is capable of running on a public and private cloud platform for self-configuring and programmable robotic systems, as well as executing various applications on different robot types. The CCRP is facilitating the improvements to QoS performance, compatibility and scalability and is providing a secure cloud computing environment for on-board robots

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

What Would You Ask to Your Home if It Were Intelligent? Exploring User Expectations about Next-Generation Homes

Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deploymen

System for improving the efficiency of wireless networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 30-31).Wireless data networks are widespread and growing quickly. As their use increases, many wireless networks are becoming congested. In addition, as wireless data capability moves into ever-smaller devices, power becomes a significant issue. This thesis presents a system that increases network bandwidth and decreases energy use without changing existing network hardware or protocols. We use specialized proxy servers to transparently modify the traffic sent over the mobile link such that the total energy used by the receiver is reduced and the effective bandwidth is increased. Our techniques include optimizing packet size, eliminating unnecessary traffic, and masking wireless packet losses. We design and implement two proxies--one for access points and one for mobile devices--which when used together, achieve up to a 20% decrease in energy and 38% increase in throughput.by Hans Robertson.M.Eng

Towards Optimising WLANs Power Saving: Novel Context-aware Network Traffic Classification Based on a Machine Learning Approach

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on Machine Learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed, Context-aware Listen Interval (CALI) power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behaviour and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. Comparative results show that more than 99% of accuracy can be achieved. Our study indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on levels of interaction in the background