In this paper, we revisit the idea of putting interfaces to sleep during packet overhearing (i.e., when there are ongoing transmissions addressed to other stations) from a practical standpoint. To this aim, we perform a robust experimental characterisation of the timing and consumption behaviour of a commercial 802.11 card. We design μNap, a local standard-compliant energy-saving mechanism that leverages micro-sleep opportunities inherent to the CSMA operation of 802.11 WLANs. This mechanism is backwards compatible and incrementally deployable, and takes into account the timing limitations of existing hardware, as well as practical CSMA-related issues (e.g., capture effect). According to the performance assessment carried out through trace-based simulation, the use of our scheme would result in a 57 reduction in the time spent in overhearing, thus leading to an energy saving of 15.8 of the activity time.
Zhang and Shin @cite_11 addressed the issue from a different standpoint with their Energy-Minimizing Idle Listening (E-MiLi). E-MiLi adaptively downclocks the card during idle periods, and reverts to full rate when an incoming frame is detected. To achieve this purpose, they need to change the physical layer (PHY) all the way down to enable downclocked detection, which severely limits the potential gains. For instance, the E-MiLi downclocking factor of 16 would yield a high power consumption in a modern card compared to its sleep state (see ).
@cite_11
2103281825 2042437844 2002377960 2649848418
WiFi interface is known to be a primary energy consumer in mobile devices, and idle listening (IL) is the dominant source of energy consumption in WiFi. Most existing protocols, such as the 802.11 power-saving mode (PSM), attempt to reduce the time spent in IL by sleep scheduling. However, through an extensive analysis of real-world traffic, we found more than 60 percent of energy is consumed in IL, even with PSM enabled. To remedy this problem, we propose Energy-Minimizing idle Listening (E-MiLi) that reduces the power consumption in IL, given that the time spent in IL has already been optimized by sleep scheduling. Observing that radio power consumption decreases proportionally to its clock rate, E-MiLi adaptively downclocks the radio during IL, and reverts to full clock rate when an incoming packet is detected or a packet has to be transmitted. E-MiLi incorporates sampling rate invariant detection, ensuring accurate packet detection and address filtering even when the receiver's sampling clock rate is much lower than the signal bandwidth. Further, it employs an opportunistic downclocking mechanism to optimize the efficiency of switching clock rate, based on a simple interface to existing MAC-layer scheduling protocols. We have implemented E-MiLi on the USRP software radio platform. Our experimental evaluation shows that E-MiLi can detect packets with close to 100 percent accuracy even with downclocking by a factor of 16. When integrated with 802.11, E-MiLi can reduce energy consumption by around 44 percent for 92 percent of users in real-world wireless networks. Reducing energy consumption is one of the most important design aspects for small form-factor mobile platforms, such as smartphones and tablets. Despite its potential for power savings, optimally leveraging system low-power sleep states during active mobile workloads, such as video streaming and web browsing, has not been fully explored. One major challenge is to make intelligent power management decisions based on, among other things, accurate system idle duration prediction, which is difficult due to the non-deterministic system interrupt behavior. In this paper, we propose a novel framework, called E2S3 (Energy Efficient Sleep-State Selection), that dynamically enters the optimal low-power sleep state to minimize the system power consumption. In particular, E2S3 detects and exploits short idle durations during active mobile workloads by, (i) finding optimal thresholds (i.e., energy break-even times) for multiple low-power sleep states, (ii) predicting the sleep-state selection error probabilities heuristically, and by (iii) selecting the optimal sleep state based on the expected reward, e.g., power consumption, which incorporates the risks of making a wrong decision We implemented and evaluated E2S3 on Android-based smartphones, demonstrating the effectiveness of the algorithm. The evaluation results show that E2S3 significantly reduces the platform energy consumption, by up to 50 (hence extending battery life), without compromising system performance. A five-fold increase in leakage current is predicted with each technology generation. While Dynamic Voltage Scaling (DVS) is known to reduce dynamic power consumption, it also causes increased leakage energy drain by lengthening the interval over which a computation is carried out. Therefore, for minimization of the total energy, one needs to determine an operating point, called the critical speed. We compute processor slowdown factors based on the critical speed for energy minimization. Procrastination scheduling attempts to maximize the duration of idle intervals by keeping the processor in a sleep shutdown state even if there are pending tasks, within the constraints imposed by performance requirements. Our simulation experiments show that the critical speed slowdown results in up to 5 energy gains over a leakage oblivious dynamic voltage scaling. Procrastination scheduling scheme extends the sleep intervals to up to 5 times, resulting in up to an additional 18 energy gains, while meeting all timing requirements. In this paper, we consider a scenario where an energy harvesting sensor continuously monitors a system and sends time-stamped status updates to a destination. The destination keeps track of the system status through the received updates. We use the metric Age of Information (AoI), the time that has elapsed since the last received update was generated, to measure the “freshness” of the status information available at the destination. Our objective is to design optimal online status update policies to minimize the long-term average AoI, subject to the energy causality constraint at the sensor. We consider three scenarios, i.e., the battery size is infinite, finite, and one unit only, respectively. For the infinite battery scenario, we adopt a best-effort uniform status update policy and show that it minimizes the long-term average AoI. For the finite battery scenario, we adopt an energy-aware adaptive status update policy, and prove that it is asymptotically optimal when the battery size goes to infinity. For the last scenario where the battery size is one, we first show that within a broadly defined class of online policies, the optimal policy should have a renewal structure. We then focus on a renewal interval, and prove that the optimal policy should have a threshold structure, i.e., if the AoI in the system is below a threshold when an energy arrival enters an empty battery, the sensor should store the energy first and then update when the AoI reaches the threshold; otherwise, it updates the status immediately. Simulation results corroborate the theoretical bounds.