379 research outputs found
Cost-efficient deployment of multi-hop wireless networks over disaster areas using multi-objective meta-heuristics
Nowadays there is a global concern with the growing frequency and magnitude of natural disasters, many of them associated with climate change at a global scale. When tackled during a stringent economic era, the allocation of resources to efficiently deal with such disaster situations (e.g., brigades, vehicles and other support equipment for fire events) undergoes severe budgetary limitations which, in several proven cases, have lead to personal casualties due to a reduced support equipment. As such, the lack of enough communication resources to cover the disaster area at hand may cause a risky radio isolation of the deployed teams and ultimately fatal implications, as occurred in different recent episodes in Spain and USA during the last decade. This issue becomes even more dramatic when understood jointly with the strong budget cuts lately imposed by national authorities. In this context, this article postulates cost-efficient multi-hop communications as a technological solution to provide extended radio coverage to the deployed teams over disaster areas. Specifically, a Harmony Search (HS) based scheme is proposed to determine the optimal number, position and model of a set of wireless relays that must be deployed over a large-scale disaster area. The approach presented in this paper operates under a Pareto-optimal strategy, so a number of different deployments is then produced by balancing between redundant coverage and economical cost of the deployment. This information can assist authorities in their resource provisioning and/or operation duties. The performance of different heuristic operators to enhance the proposed HS algorithm are assessed and discussed by means of extensive simulations over synthetically generated scenarios, as well as over a more realistic, orography-aware setup constructed with LIDAR (Laser Imaging Detection and Ranging) data captured in the city center of Bilbao (Spain)
FireFly Mosaic: A Vision-Enabled Wireless Sensor Networking System
Abstract â With the advent of CMOS cameras, it is now possible to make compact, cheap and low-power image sensors capable of on-board image processing. These embedded vision sensors provide a rich new sensing modality enabling new classes of wireless sensor networking applications. In order to build these applications, system designers need to overcome challanges associated with limited bandwith, limited power, group coordination and fusing of multiple camera views with various other sensory inputs. Real-time properties must be upheld if multiple vision sensors are to process data, com-municate with each other and make a group decision before the measured environmental feature changes. In this paper, we present FireFly Mosaic, a wireless sensor network image processing framework with operating system, networking and image processing primitives that assist in the development of distributed vision-sensing tasks. Each FireFly Mosaic wireless camera consists of a FireFly [1] node coupled with a CMUcam3 [2] embedded vision processor. The FireFly nodes run the Nano-RK [3] real-time operating system and communicate using the RT-Link [4] collision-free TDMA link protocol. Using FireFly Mosaic, we demonstrate an assisted living application capable of fusing multiple cameras with overlapping views to discover and monitor daily activities in a home. Using this application, we show how an integrated platform with support for time synchronization, a collision-free TDMA link layer, an underlying RTOS and an interface to an embedded vision sensor provides a stable framework for distributed real-time vision processing. To the best of our knowledge, this is the first wireless sensor networking system to integrate multiple coordinating cameras performing local processing. I
Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review
Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference
Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated
Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)
Towards an intelligent and supportive environment for people with physical or cognitive restrictions
AmbienNet environment has been developed with the aim
of demonstrating the feasibility of accessible
intelligent environments designed to support people with
disabilities and older persons living independently. Its main
purpose is to examine in depth the advantages and disadvantages
of pervasive supporting systems based on the paradigm of
Ambient Intelligence for people with sensory, physical or
cognitive limitations. Hence diverse supporting technologies
and applications have been designed in order to test their
accessibility, ease of use and validity. This paper presents
the architecture of AmbienNet intelligent environment and
an intelligent application to support indoors navigation for
smart wheelchairs designed for validation purposes.Ministerio de EducaciĂłn y Ciencia TIN2006-15617-C[01,02,03
Differential Evolution in Wireless Communications: A Review
Differential Evolution (DE) is an evolutionary computational
method inspired by the biological processes of evolution and mutation. DE has
been applied in numerous scientific fields. The paper presents a literature review
of DE and its application in wireless communication. The detailed history,
characteristics, strengths, variants and weaknesses of DE were presented. Seven
broad areas were identified as different domains of application of DE in wireless
communications. It was observed that coverage area maximisation and energy
consumption minimisation are the two major areas where DE is applied.
Others areas are quality of service, updating mechanism where candidate positions
learn from a large diversified search region, security and related field applications.
Problems in wireless communications are often modelled as multiobjective
optimisation which can easily be tackled by the use of DE or hybrid of
DE with other algorithms. Different research areas can be explored and DE will
continue to be utilized in this contex
SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network
Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes
-Means++: Adapt- Driven by Energy and Distance Quality Probabilities Based on -Means++ for the Stable Election Protocol (SEP)
The adaptive probability formalized in Adapt- is
developed based on the remaining number of SNs and optimal clustering
, yet does not implement
the probabilistic ratios of energy and distance factors in the network.
Furthermore, Adapt- does not localize cluster-heads in the first round
properly because of its reliance on distance computations defined in LEACH,
that might result in uneven distribution of cluster-heads in the WSN area and
hence might at some rounds yield inefficient consumption of energy. This paper
utilizes \nolinebreak{\small{-}means\small{++}} and Adapt- to propose
\nolinebreak{\small{-}means\small{++}}
clustering algorithm that better manages the distribution of cluster-heads and
produces an enhanced performance. The algorithm employs an optimized
cluster-head election probability developed based on energy-based
and distance-based P\!\!\!_{\psi(j,i)} quality probabilities
along with the adaptive probability , utilizing the
energy and distance optimality d\!_{\text{\tiny{opt}}} factors.
Furthermore, the algorithm utilizes the optimal clustering
derived in Adapt- to perform adaptive
clustering through
\nolinebreak{\small{-}means\small{++}}. The
proposed \nolinebreak{{\small{-}}means{\small{++}}} is compared with the
energy-based algorithm \nolinebreak{{\small{-}}means{\small{++}}} and distance-based
\nolinebreak{{\small{-}}means{\small{++}}} algorithm, and has
shown an optimized performance in term of residual energy and stability period
of the network
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