Search results for "wireless sensor"

Efficient Hybrid Emergency Aware MAC Protocol for Wireless Body Sensor Networks

2018

International audience; In Body Sensor Networks (BSNs), two types of events should be addressed: periodic and emergency events. Traffic rate is usually low during periodic observation, and becomes very high upon emergency. One of the main and challenging requirements of BSNs is to design Medium Access Control (MAC) protocols that guarantee immediate and reliable transmission of data in emergency situations, while maintaining high energy efficiency in non-emergency conditions. In this paper, we propose a new emergency aware hybrid DTDMA/DS-CDMA protocol that can accommodate BSN traffic variations by addressing emergency and periodic traffic requirements. It takes advantage of the high delay …

Knowledge-based verification of concatenative programming patterns inspired by natural language for resource-constrained embedded devices

2020

We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns. The methodology is supported by a rule-based sys…

Managing sensor data streams in a smart home application

2020

A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programmi…

Optimization of flat area coverage under connectivity constraint in wireless sensor networks

2022

A wireless sensor network consists of a set of small autonomous units that interact via a network built by their communication modules. They observe their environment, capture information, then manage this information according to their computing and/or storage capacity. To effectively accomplish their task(s), they need to cover as much of the area of interest as possible. It is therefore essential to quantify the quality of their coverage. In this thesis, we therefore seek to best cover an area of interest, with a precise number of sensors. While taking into account the possible overlaps between sensors, we first deploy in a zone of regular dimensions and evaluate the exact coverage using…

A greedy perturbation approach to accelerating consensus algorithms and reducing its power consumption

2011

The average consensus is part of a family of algorithms that are able to compute global statistics by only using local data. This capability makes these algorithms interesting for applications in which these distributed philosophy is necessary. However, its iterative nature usually leads to a large power consumption due to the repetitive communications among the iterations. This drawback highlights the necessity of minimizing the power consumption until consensus is reached. In this work, we propose a greedy approach to perturbing the connectivity graph, in order to improve the convergence time of the consensus algorithm while keeping bounded the power consumption per iteration step. These …

LoRa-Based Sensor Node Energy Consumption with Data Compression

2021

In this paper simple temporal compression algorithms' efficiency to reduce LoRa-based sensor node energy consumption has been evaluated and measured. It is known that radio transmission is the most energy consuming operation in a wireless sensor node. In this paper three lightweight compression algorithms are implemented in an embedded LoRa platform to compress sensor data in on-line mode and the overall energy consumption is measured. Energy consumption is compared to the situation without implementing any compression algorithm. The results show that a simple compression algorithm is an effective method to improve the battery powered sensor node lifetime. Despite the radio transmission's h…

Set-valued consensus for distributed clock synchronization

2009

This paper addresses the clock synchronization problem in a wireless sensor network (WSN) and proposes a distributed solution that consists of a form of consensus, where agents are able to exchange data representing intervals or sets. The solution is based on a centralized algorithm for clock synchronization, proposed by Marzullo, that determines the smallest interval that is in common with the maximum number of measured intervals. We first show how to convert such an algorithm into a problem involving only operations on sets, and then we convert it into a set–valued consensus. The solution is valid for more general scenarios where agents have uncertain measures of e.g. the position of an o…

Wireless Sensor Networks for Marine Environment Monitoring

2008

Bio-inspired Sensory Data Aggregation

2013

The Ambient Intelligence (AmI) research field focuses on the design of systems capable of adapting the surrounding environmental conditions so that they can match the users needs, whether those are consciously expressed or not [4][1].

Knowledge Extraction from Environmental Data Through a Cognitive Architecture

2008

Wireless Sensor Networks represent a novel technology which is expected to experience a dramatic diffusion thanks to the promise to be a pervasive sensory means; however, one of the issues limiting their potential growth relies in the difficulty of managing and interpreting huge amounts of collected data. This paper proposes a cognitive architecture for the extraction of high-level knowledge from raw data through the representation of processed data in opportune conceptual spaces. The presented framework interposes a conceptual layer between the subsymbolic one, devoted to sensory data processing, and the symbolic one, aimed at describing the environment by means of a high level language. T…