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Self-sustainability for energy scavenging networks is a crucial step in modern sensor network developments. Most existing analyses are however tailored to a single transmitting node or are difficult to map into practical protocol designs. Here, we offer a comprehensive framework for self-sufficient sensor networks powered by renewable energy sources. To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the outer step, we provide optimal energy management policies to make the system self-sufficient, given the the statistical description of the energy source. Our framework permits to gauge the impact of key sensor network parameters, such as the battery size, the harvester size (e.g., solar panel), the information transmission rate and the nodes' duty cycle. In addition, the closed form solution of the inner optimization proble...
—Recent advances in environmental energy harvesting technologies have provided great potentials for traditional battery-powered sensor networks to achieve perpetual operations. Due to dynamics from the temporal profiles of ambient energy sources, most of the studies so far have focused on designing and optimizing energy management schemes on single sensor node, but overlooked the impact of spatial variations of energy distribution when sensors work together at different locations. To design a robust sensor network, in this paper, we use mobility to circumvent communication bottlenecks caused by spatial energy variations. We employ a mobile collector, called SenCar to collect data from designated sensors and balance energy consumptions in the network. To show spatial-temporal energy variations, we first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we present a two-step approach for mobile data collection. First, we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. Second, we focus on designing distributed algorithms to achieve maximum network utility by adjusting data rates, link scheduling and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms can converge to optimality very fast and validate its convergence in case of node failure. We also show advantages of our framework can adapt to spatial-temporal energy variations and demonstrate its superiority compared to the network with static data sink.
International Journal of Electrical and Computer Engineering (IJECE)
In this paper, we consider a remote environment with randomly deployed sensor nodes, with an initial energy of E0 (J) and a solar panel. A hierarchical clustering technique is implemented. At each round, the normal nodes send the sensed data to the nearest cluster head (CH) which is chosen on the probability value. Data after aggregation at CHs is sent to the base station (BS). CH requires more energy than normal nodes. Here, we energize only CHs if their energy is less than 5% of its initial value with the use of solar energy. We evaluate parameters like energy consumption, the lifetime of the network, and data packets sent to CH and BS. The obtained results are compared with existing techniques. The proposed protocol provides better energy efficiency and network lifetime. The results show increased stability with delayed death of the first node. The network lifetime of the proposed protocol is compared to the multi-level hybrid energy efficient distributed (MLHEED) technique and l...
2003
Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.
ACM Transactions in Embedded Computing Systems, 2007
Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.
… of wireless ad hoc, sensor and …, 2006
Environmentally-powered wireless sensor networks (WSNs) exploit renewable energy sources to make the lifetime of sensor nodes theoretically unlimited. This perspective requires a paradigm shift in the design of energy-aware WSNs: Instead of maximizing the lifetime under given energy constraints, we need to maximize the workload that can be sustained by a given distribution of environmental power. This paper formulates the maximum energetically sustainable workload problem (MESW) and we shows that it can be cast into an instance of a modified max-flow problem.
2010
Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, time variations in recharging rate presents a new challenge. In this paper, we first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes when a DAG routing structure is given. Then, we analytically study the key properties of the optimal DAG(s) and propose a mechanism for constructing a DAG that can support high network utility. Moreover, fluctuations in recharging can happen at a faster timescale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42% on the average while significantly improving the network utility.
Internet-of-things enabled applications are increasingly popular and are expected to spread even more in the next few years. Energy efficiency is fundamental to support the widespread use of such systems. This paper presents a practical framework for the development and the evaluation of low-power Wireless Sensor Networks equipped with energy harvesting, aiming at energy-autonomous applications. An experimental case study demonstrates the capabilities of the solution.
nternational journal of communication networks and information security, 2022
Power management strategies are extremely important in Wireless Sensor Networks (WSNs). The objective is to make the nodes operate as long as possible. In the same context, in this article, our aim is to provide the optimal transmission power to maximize the network lifetime using the Orthogonal Multiple Access Channel (OMAC) in Harvesting System (HS). We consider that the nodes have direct communication with a Fusion Center (FC) with causal Channel Side Information (CSI) at the sender and receiver. We begin the analysis by considering a single transmitter node powered by a rechargeable battery with limited capacity energy. Afterward, we generalize the analysis with M transmitter nodes. In both cases, the transmitters are able to harvest energy from nature. Eventually, we show the viability of our approach in simulations results.
2010
Abstract Renewable energy sources can be attached to sensor nodes to provide energy replenishment for prolonging the lifetime of sensor networks. However, for networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to battery capacity limitations, while aggressive usage of energy may result in reduced coverage or connectivity for certain time periods.
Lecture notes in networks and systems, 2022
in this paper we formulate the problem of maximizing the lifetime of sensor networks with a mobile sink and solar energy supply. It is known that neither mobile sink nor solar energy supply can achieve the maximal possible sensor network lifetime when used independently. We show by computational experiments on the optimization problem that using jointly these two techniques lead to a significant increase of the network lifetime.
Wireless Communications and Mobile Computing, 2019
IEEE Transactions on Wireless Communications, 2013
Future deployments of wireless sensor network (WSN) infrastructures for environmental or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal, photovoltaic) in order to substantially increase their autonomy. In this paper we derive conditions for energy neutrality, i.e. perpetual energy autonomy per sensor node, by balancing the node's expected energy consumption with its expected energy harvesting capability. Our analysis assumes a uniformly-formed WSN, i.e. a network comprising identical transmitter sensor nodes and identical receiver/relay sensor nodes with a balanced cluster-tree topology. The proposed framework is parametric to: (i) the duty cycle for the network activation; (ii) the number of nodes in the same tier of the cluster-tree topology; (iii) the consumption rate of the receiver node(s) that collect (and possibly relay) data along with their own; (iv) the marginal probability density function (PDF) characterizing the data transmission rate per node; (v) the expected amount of energy harvested by each node. Based on our analysis, we obtain the number of nodes leading to the minimum energy harvesting requirement for each tier of the WSN cluster-tree topology. We also derive closed-form expressions for the difference in the minimum energy harvesting requirements between four transmission rate PDFs in function of the WSN parameters. Our analytic results are validated via experiments using TelosB sensor nodes and an energy measurement testbed. Our framework is useful for feasibility studies on energy harvesting technologies in WSNs and for optimizing the operational settings of hierarchical WSN-based monitoring infrastructures prior to time-consuming testing and deployment within the application environment.
2008
Sensor networks differ from traditional wireless networks in several respects. Unlike handheld wireless devices which can be recharged at reasonable frequent intervals, sensor nodes must operate autonomously for much longer durations. Energy supply thus remains an open challenge in sensor networks because unfettered deployment rules out traditional wall socket supplies and batteries with acceptable form factor and cost constraints do not yield the lifetimes desired by most applications. Wireless sensor networks (WSNs) research has predominantly assumed the use of a portable and limited energy source, namely batteries, to power sensors. Without energy, a sensor is essentially useless and cannot contribute to the utility of the network as a whole. Consequently, substantial research efforts have been spent on designing energy-efficient networking protocols to maximize the lifetime of WSNs. However, there are emerging WSN applications where sensors are required to operate for much longe...
IEEE Wireless Communications, 2000
Energy harvesting technologies are required for autonomous sensor networks for which using a power source from a fixed utility or manual battery recharging is infeasible. An energy harvesting device (e.g., a solar cell) converts different forms of environmental energy into electricity to be supplied to a sensor node. However, since it can produce energy only at a limited rate, energy
Computer Communications, 2007
A new class of wireless sensor networks that harvest power from the environment is emerging because of its intrinsic capability of providing unbounded lifetime. While a lot of research has been focused on energy-aware routing schemes tailored to battery-operated networks, the problem of optimal routing for energy harvesting wireless sensor networks (EH-WSNs) has never been explored. The objective of routing optimization in this context is not extending network lifetime, but maximizing the workload that can be autonomously sustained by the network.
Future deployments of wireless sensor network (WSN) infrastructures for environmental or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal, photovoltaic) in order to substantially increase their autonomy. In this paper we derive conditions for energy neutrality, i.e. perpetual energy autonomy per sensor node, by balancing the node's expected energy consumption with its expected energy harvesting capability. Our analysis assumes a uniformly-formed WSN, i.e. a network comprising identical transmitter sensor nodes and identical receiver/relay sensor nodes with a balanced cluster-tree topology. The proposed framework is parametric to: (i) the duty cycle for the network activation; (ii) the number of nodes in the same tier of the cluster-tree topology; (iii) the consumption rate of the receiver node(s) that collect (and possibly relay) data along with their own; (iv) the marginal probability density function (PDF) characterizing the data transmission rate per node; (v) the expected amount of energy harvested by each node. Based on our analysis, we obtain the number of nodes leading to the minimum energy harvesting requirement for each tier of the WSN cluster-tree topology. We also derive closed-form expressions for the difference in the minimum energy harvesting requirements between four transmission rate PDFs in function of the WSN parameters. Our analytic results are validated via experiments using TelosB sensor nodes and an energy measurement testbed. Our framework is useful for feasibility studies on energy harvesting technologies in WSNs and for optimizing the operational settings of hierarchical WSN-based monitoring infrastructures prior to time-consuming testing and deployment within the application environment.
international conference on hardware/software codesign and system synthesis, 2012
Wireless sensor networks allow scientists to gather data from remote, difficult to access, and dangerous locations. However, maintenance of aging networks and removal of obsolete or inactive nodes containing toxic materials is expensive and time consuming. Moreover, node lifespan is generally constrained by the reliability of the batteries used in most deployments, especially in the presence of extreme variation in environmental conditions such as temperature and humidity. We consider the problem of designing wireless sensor networks capable of indefinite deployment periods measured in decades, not months. We describe the architectural and capability implications of eliminating batteries from sensor networks and instead relying on opportunistic energy scavenging. Sensor nodes using ambient energy sources become temporarily active at unpredictable but possibly correlated times. In this paper, we use wind power as an example of such a power source, which we model using temporally and spatially correlated random processes. Such models can be built using historical measurements over a geographical range. We describe a method to use energy models in the design of latency-optimized and cost-constrained battery-less wireless sensor networks, and explain the required changes to network architecture, communication protocol, and node hardware. In the context of environmental monitoring applications, we compare the performance of a network designed and managed using our techniques with that of existing design styles.
Journal of Low Power Electronics and Applications
Wireless Sensor Networks (WSNs) are considered to be among the most important scientific domains. Yet, the exploitation of WSNs suffers from the severe energy restrictions of their electronic components. For this reason there are numerous scientific methods that have been proposed aiming to achieve the extension of the lifetime of WSNs, either by energy saving or energy harvesting or through energy transfer. This study aims to analytically examine all of the existing hardware-based and algorithm-based mechanisms of this kind. The operating principles of 48 approaches are studied, their relative advantages and weaknesses are highlighted, open research issues are discussed, and resultant concluding remarks are drawn.
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