Abstract

Energy autonomy for Wireless Sensor Networks (WSNs) is a key to involve industry stakeholders willing to spend billions on the Internet of Things. By offering the lifetime of only a few years, traditional battery powered WSNs are neither practical nor profitable due to their high maintenance cost. Powering WSNs with energy harvesters can overcome this limitation and increase mean time-to-maintenance to tens of years. However, the primary challenge in realizing an energy neutral operation is to reduce the consumed energy drastically to match with the harvested energy. This dissertation proposes techniques to minimize the overhead of two main activities: communication and sampling. It does so by making a key observation: a plethora of applications can accept low accuracy of sensed phenomenon without sacrificing the application requirements. This fact enables us to reduce consumed energy by radically revising the network stack design, all the way from the application layer to underlying hardware. At the application layer, the relaxed requirements make it possible to propose techniques to reduce the data exchanges among the nodes, the most power hungry operation in WSNs. For example, we propose a simple yet efficient prediction based data collection technique called Derivative-Based Prediction (DBP) that enables data suppression up to 99%. With the remaining ultra-low application data rate, a full system-wide evaluation reveals that the dominating overhead of the lower layers greatly limits the gains enabled by DBP. A cross-layer optimization of the network stack is then designed specifically to strip off the unnecessary overhead to gain one order of magnitude longer lifetime. Although a huge saving in relative terms, the resulting power consumption is still much higher than tens of microwatts, the power usually achievable from a reasonably sized harvester deployed in an indoor environment. Therefore, we consider a novel combination of hardware components to further reduce power consumption. Our work demonstrates that using wake-up receivers along with DBP results in long idle periods with only rare occurrences of power hungry states such as radio transmissions and receptions. Low power modes, provided by various components of the underlying hardware platform, are adopted in the idle periods to conserve energy. In concrete real-world case studies, the lifetime is estimated to improve by two orders of magnitude. Thanks to the software and hardware features proposed above, the overall power consumption is reduced to a point where the sampling cost constitutes a significant portion of it. To reduce the cost of sampling, we introduce the concept of Model-based Sensing in which we push prediction based data collection as close as possible to the hardware sensing elements. This hardware-software co-design results in a system that consumes only a few microwatts, a point where even harvesters deployed in challenging indoor conditions can sustain the operation of nodes. This dissertation advances the state of art on energy efficient WSNs in several dimensions. First, it bridges the gap between theory and practice by providing the first ever system-wide evaluation of prediction based data collection in real-world WSNs. Second, new software based optimizations and novel hardware components are proposed that can deliver three orders of magnitude reduction in power consumption. Third, it provides tools to estimate the harvestable energy in real WSNs. By using these tools, the work highlights that the energy consumed by the proposed mechanisms is indeed lower than the energy harvested. By closing the gap between supply and demand of energy, the dissertation takes a concrete step in the direction of achieving completely energy neutral WSNs.