An environmental energy harvesting framework for sensor networks (original) (raw)
Related papers
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.
European Journal of Electrical Engineering and Computer Science, 2022
Wireless Sensor Networks (WSN) have largely integrated all areas, including the military and civil fields. Their main limitation is their energy resources, which are very limited. Charging or replacing their batteries is often complicated or impossible, due to the high costs involved. The development of new approaches to energy management techniques for these autonomous systems has identified two strategic categories of energy management classification. The first category "Software" targets the development of algorithms for routing protocols to make transmissions smarter and more energy-efficient. The second category "Hardware", focused more on new energy recovery technologies, has drawn the attention of academicians and industrialists because they bring a new manner of energy storage with life extension performance. Furthermore, this category has inspired new ways of supporting WSN administered applications such as real-time processes. In this paper, we review d...
Autonomous Agents and Multi-Agent Systems, 2012
This paper reports on the development of a multi-agent approach to long-term information collection in networks of energy harvesting wireless sensors. In particular, we focus on developing energy management and data routing policies that adapt their behaviour according to the energy that is harvested, in order to maximise the amount of information collected given the available energy budget. In so doing, we introduce a new energy management technique, based on multi-armed bandit learning, that allows each agent to adaptively allocate its energy budget across the tasks of data sampling, receiving and transmitting. By using this approach, each agent can learn the optimal energy budget settings that give it efficient information collection in the long run. Then, we propose two novel decentralised multi-hop algorithms for data routing. The first proveably maximises the information throughput in the network, but can sometimes involve high communication cost. The second algorithm provides near-optimal performance, but with reduced computational and communication costs. Finally, we demonstrate that, by using our approaches for energy management and routing, we can achieve a 120% improvement in long-term information collection against state-of-the-art benchmarks.
Studying the Feasibility of Energy Harvesting in a Mobile Sensor Network
2003
We study the feasibility of extending the lifetime of a wireless sensor network by exploiting mobility. In our system, a small percentage of network nodes are autonomously mobile, allowing them to move in search of energy, recharge, and deliver energy to immobile, energy-depleted nodes. We term this approach energy harvesting. We characterize the problem of uneven energy consumption, suggest energy harvesting as a possible solution, and provide a simple analytical framework to evaluate energy consumption and our scheme. Data from initial feasibility experiments using energy harvesting show promising results.
Lecture Notes in Computer Science, 2016
In this paper, we study photovoltaic energy harvesting in wireless sensor networks. We build a harvesting analytical model for a single node, linking three components: the environment, the battery, and the application. Given information on two of the components, limits on the third one can be determined. To test this model, we adopt several use cases with various indoor and outdoor locations, battery types, and application requirements. Results show that, for pre-defined application parameters, we are able to determine the acceptable node duty cycle given a specific battery, and vice versa. Moreover, the suitability of the deployment environment (outdoor, well lighted indoor, poorly lighted indoor) for different application characteristics and battery types is discussed.
Power management in energy harvesting sensor networks
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.
An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks
—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.
Energies
Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in...
Adaptive duty cycling for energy harvesting systems
2006
Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energyneutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used.
Optimized Framework for Collection of Data Mobility in Energy Harvesting Wireless Sensor Network
2018
We introduce environment as energy harvesting technologies. energy harvesting can be described as mechanism used to generate energy from network ambient we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We present three-hop step method for mobile data collection in energy harvesting sensor network. Firstly we collect the location status of our nodes and SenCar and on the basis of its information we partition the nodes in clusters of different regions. We then develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. At last, 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.Our analysis shows that we achieved our aim of maximum network utility with an optimized framework.