11,117 research outputs found
Optimal data collection in wireless sensor networks with correlated energy harvesting
We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile
sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further
assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
An Optimal Control Approach for the Data Harvesting Problem
We propose a new method for trajectory planning to solve the data harvesting
problem. In a two-dimensional mission space, mobile agents are tasked with
the collection of data generated at stationary sources and delivery to a
base aiming at minimizing expected delays. An optimal control formulation of
this problem provides some initial insights regarding its solution, but it is
computationally intractable, especially in the case where the data generating
processes are stochastic. We propose an agent trajectory parameterization in
terms of general function families which can be subsequently optimized on line
through the use of Infinitesimal Perturbation Analysis (IPA). Explicit results
are provided for the case of elliptical and Fourier series trajectories and
some properties of the solution are identified, including robustness with
respect to the data generation processes and scalability in the size of an
event set characterizing the underlying hybrid dynamic system
A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks
In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs
Social issues of power harvesting as key enables of WSN in pervasive computing
Pervasive systems have gained popularity and open the door to new applications that will improve the quality of life of the users. Additionally, the implementation of such systems over an infrastructure of Wireless Sensor Networks has been proven to be very powerful. To deal with the WSN problems related to the battery of the elements or nodes that constitute the WSN, Power Harvesting techniques arise as good candidates. With PH each node can extract the energy from the surrounding environment. However, this energy source could not be constant, affecting the continuity and quality of the services provided. This behavior can have a negative impact on the user's perception about the system, which could be perceived as unreliable or faulty. In the current paper, some related works regarding pervasive systems within the home environment are referenced to extrapolate the conclusions and problems to the paradigm of Power Harvesting Pervasive Systems from the user perspective. Besides, the paper speculates about the approach and methods to overcome these potential problems and presents the design trends that could be followed.<br/
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
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