911 research outputs found
A Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) for Energy and Network Lifetime Maximization under Coverage Constrained Problems in Heterogeneous Wireless Sensor Networks
Network lifetime maximization of Wireless Heterogeneous Wireless Sensor Networks (HWSNs) is a difficult problem. Though many methods have been introduced and developed in the recent works to solve network lifetime maximization. However, in HWSNs, the energy efficiency of sensor nodes becomes also a very difficult issue. On the other hand target coverage problem have been also becoming most important and difficult problem. In this paper, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent HWSNs with each vertex representing the assignment of a sensor nodes in a subset. At the same time, Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K-Coverage (KC) known as TFMGA-MDCCKC. Based on gene and chromosome information from the TFMGA, the gene seeks an optimal path on the construction graph model that maximizes the MDCCKC. In TFMGA gene thus focuses on finding one more connected covers and avoids creating subsets particularly. A local search procedure is designed to TFMGA thus increases the search efficiency. The proposed TFMGA-MDCCKC approach has been applied to a variety of HWSNs. The results show that the TFMGA-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that proposed TFMGA-MDCCKC approach performs better than Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method and the performance of the TFMGA-MDCCKC approach is closer to the energy-conserving strategy
A survey of network lifetime maximization techniques in wireless sensor networks
Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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Spreadable Connected Autonomic Networks (SCAN)
A Spreadable Connected Autonomic Network (SCAN) is a mobile network that automatically maintains its own connectivity as nodes move. We envision SCANs to enable a diverse set of applications such as self-spreading mesh networks and robotic search and rescue systems. This paper describes our experiences developing a prototype robotic SCAN built from commercial, off-the-shelf hardware, to support such applications. A major contribution of our work is the development of a protocol, called SCAN1, which maintains network connectivity by enabling individual nodes to determine when they must constrain their mobility in order to avoid disconnecting the network. SCAN1 achieves its goal through an entirely distributed process in which individual nodes utilize only local (2-hop) knowledge of the network's topology to periodically make a simple decision: move, or freeze in place. Along with experimental results from our hardware testbed, we model SCAN1's performance, providing both supporting analysis and simulation for the efficacy of SCAN1 as a solution to enable SCANs. While our evaluation of SCAN1 in this paper is limited to systems whose capabilities match those of our testbed, SCAN1 can be utilized in conjunction with a wide-range of potential applications and environments, as either a primary or backup connectivity maintenance mechanism
Recommended from our members
Spreadable Connected Autonomic Networks (SCAN)
A Spreadable Connected Autonomic Network (SCAN) is a mobile network that automatically maintains its own connectivity as nodes move. We envision SCANs to enable a diverse set of applications such as self-spreading mesh networks and robotic search and rescue systems. This paper describes our experiences developing a prototype robotic SCAN built from commercial, off-the-shelf hardware, to support such applications. A major contribution of our work is the development of a protocol, called SCAN1, which maintains network connectivity by enabling individual nodes to determine when they must constrain their mobility in order to avoid disconnecting the network. SCAN1 achieves its goal through an entirely distributed process in which individual nodes utilize only local (2-hop) knowledge of the network's topology to periodically make a simple decision: move, or freeze in place. Along with experimental results from our hardware testbed, we model SCAN1's performance, providing both supporting analysis and simulation for the efficacy of SCAN1 as a solution to enable SCANs. While our evaluation of SCAN1 in this paper is limited to systems whose capabilities match those of our testbed, SCAN1 can be utilized in conjunction with a wide-range of potential applications and environments, as either a primary or backup connectivity maintenance mechanism
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
HARDWARE AND SOFTWARE ARCHITECTURES FOR ENERGY- AND RESOURCE-EFFICIENT SIGNAL PROCESSING SYSTEMS
For a large class of digital signal processing (DSP) systems, design and implementation of hardware and software is challenging due to stringent constraints on energy and resource requirements. In this thesis, we develop methods to address this challenge by proposing new constraint-aware system design methods for DSP systems, and energy- and resource-optimized designs of key DSP subsystems that are relevant across various application areas. In addition to general methods for optimizing energy consumption and resource utilization, we present streamlined designs that are specialized to efficiently address platform-dependent constraints.
We focus on two specific aspects in development of energy- and resource-optimized design techniques:
(1) Application-specific systems and architectures for energy- and resource- efficient design.
First, we address challenges in efficient implementation of wireless sensor network building energy monitoring systems (WSNBEMSs). We develop new energy management schemes in order to maximize system lifetime for WSNBEMSs, and demonstrate that system lifetime can be improved significantly without affecting monitoring accuracy.
We also present resource efficient, field programmable gate array (FPGA) architecture for implementation of orthogonal frequency division multiplexing (OFDM) systems. We have demonstrated that our design provides at least 8.8% enhancement in terms of resource efficiency compared to Xilinx FFT v7.1 within the same OFDM configuration.
(2) Dataflow-based methods for structured design and implementation of energy- and resource- efficient DSP systems.
First, we introduce a dataflow-based design approach based on integrating interrupt-based signal acquisition in context of parameterized synchronous dataflow (PSDF) modeling. We demonstrate that by applying our approach, energy- and resource-efficient embedded software can be derived systematically from high level models of dynamic, data-driven applications systems (DDDASs) functional structure.
Also, we present an in-depth development of lightweight dataflow-Verilog (LWDF-V), which is an integration of the LWDF programming model with the Verilog hardware description language (HDL), and we demonstrate the utility of LWDF-V for design and implementation of digital systems for signal processing. We emphasize efficient of LWDF with HDLs, and emphasize the application of LWDF-V to design DSP systems with dynamic parameters on FPGA platforms
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