SolarCode: Utilizing Erasure Codes for Reliable Data Delivery in Solar-powered Wireless Sensor Networks

Conference PaperinProceedings - IEEE INFOCOM · April 2010with17 Reads
DOI: 10.1109/INFCOM.2010.5462283 · Source: IEEE Xplore
Conference: INFOCOM, 2010 Proceedings IEEE
  • 10.39 · University of Illinois, Urbana-Champaign
  • 2nd Lu Su
    11.02 · University at Buffalo, The State University of New York
  • 33.44 · University of Illinois, Urbana-Champaign

Solar-powered sensor nodes have incentive to spend extra energy, especially when the battery is fully charged, because this energy surplus would be wasted otherwise. In this paper, we consider the problem of utilizing such energy surplus to adaptively adjust the redundancy level of erasure codes used in communication, so that the delivery reliability is improved while the network lifetime is still conserved. We formulate the problem as maximizing the end-to-end packet delivery probability under energy constraints. This formulated problem is hard to solve because of the combinatorics involved and the special curvature of its objective function. By exploiting its inherent properties, we propose an effective solution called SolarCode, which has a constant approximation ratio. We evaluate SolarCode in the context of our solar-powered sensor network testbed. Experiments show that SolarCode is successful in utilizing energy surplus and leads to higher data delivery reliability.

    • "Predictable-profile: In [27], [29], duty cycle adaptations (mostly for single nodes) are considered. For a network, various metrics are considered including data collection rates [15], end-to-end packet delivery probability [48], data retrieval rate [49], and routing efficiency [33], [50]. Per-slot short-term predictions are assumed in [34]. "
    [Show abstract] [Hide abstract] ABSTRACT: Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as indoor light energy. We focus on characterizing the energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for determining time fair energy allocation in systems with predictable energy inputs, as well as in systems where energy inputs are stochastic.
    Full-text · Conference Paper · May 2011
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  • [Show abstract] [Hide abstract] ABSTRACT: Solar sensor nodes, equipped with micro-solar subsystems [1], provide a new way to harvest ambient energy and also bring new challenges. This paper constructs an Energy Efficient Data Aggregation Tree (EDAT) based on a Maximal Weighted Connected Dominator Set (MaCDS). EDAT arms to prolong the network lifetime by minimizing the difference of energy consumption among solar nodes while we consider that harvested energy H{\mathcal{H}} randomly and uniformly distributes in the interval [Hmin,Hmax]{[{\mathcal{H}}_{min},{\mathcal{H}}_{max}]}. The total energy consumption difference of EDAT is at most \frac5[`(H)]|S|2n-1\frac{5{\overline{\mathcal{H}}}|{\mathcal{S}}|^2}{n-1}, where [`(H)]=|[Hmin,Hmax]|{\overline{\mathcal{H}}}=|{[{\mathcal{H}}_{min},{\mathcal{H}}_{max}]}| and S{\mathcal{S}} is the dominator set.
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  • [Show abstract] [Hide abstract] ABSTRACT: Energy is the most limiting factor in wireless sensor networks. Harvesting solar energy is a feasible solution to overcome the energy-constraint in some applications. It enables a theoretically infinite network lifetime, sustaining a mode of operation termed energy neutral consumption rate. The challenge arises, how can the harvested energy be utilized to maximize the performance of the sensor network. Considering a field monitoring application the performance is measured as the sustained sampling rate of the sensors. Maximizing the sampling rate needs to take the spatio-temporal distribution of load and energy into account, to prevent the overloading of nodes. In [12] they introduced a optimal, theoretical solution based on perfect global knowledge. In this paper we propose the solar-aware distributed flow (SDF) approach. SDF enables each node to predict the harvested energy, calculate a sustainable flow and control its local neighborhood. Extensive simulations confirmed that SDF achieves over 80% of the theoretical optimum, while introducing negligible overhead.
    Preview · Conference Paper · Oct 2011
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