Distributed traces contain valuable information but are often massive in volume, posing a core challenge in tracing framework design: balancing the tradeoff between preserving essential trace information and reducing trace volume. To address this tradeoff, previous approaches typically used a '1 or 0' sampling strategy: retaining sampled traces while completely discarding unsampled ones. However, based on an empirical study on real-world production traces, we discover that the '1 or 0' strategy actually fails to effectively balance this tradeoff.

Battery-free wireless sensor network (BF-WSN) (including energy harvesting network and energy rechargeable network) is a new network architecture that has been proposed in recent years to solve the lifetime limitation problem of conventional WSNs. Battery-free sensor nodes can harvest energy from environmental energy resources or from artificial power stations. Thus, the lifetime of a BF-WSN is unlimited in terms of energy. The specific properties of BF-WSNs have brought new challenges in fundamental issues, such as energy management, networking, and data acquisition, which means the existing algorithms in WSNs cannot be adopted directly. The BF-WSN can be regarded as a totally new topic in Internet of Things (IoT) and has attracted much attention from researchers. Many algorithms have been proposed to solve the fundamental problems in BF-WSNs. The objective of this survey is to comprehensively summarize and analyze the existing works. In this survey, we first introduce the existing algorithms from three fundamental aspects, including energy management, networking, and data acquisition. Then, we present some specific applications of BF-WSNs.

Age of Information (AoI), a metric measuring the information freshness, has drawn increased attention due to its importance in monitoring applications in which nodes send timestamped status updates to interested recipients, and timely updates about phenomena are important. In this work, we consider the AoI minimization scheduling problem in multihop energy harvesting (EH) wireless sensor networks (WSNs). We design the generation time of updates for nodes and develop transmission schedules under both protocol and physical interference models, aiming at achieving minimum peak AoI and average AoI among all nodes for a given time duration. We prove that it is an NP-Hard problem and propose an energy-adaptive, distributed algorithm called the minimizing AoI scheduling algorithm for general network (MAoIG). We derive its theoretical upper bounds for the peak and average AoI and a lower bound for peak AoI. The numerical results validate that MAoIG outperforms all of the baseline schemes in all scenarios and that the experimental results tightly track the theoretical upper bound optimal solutions while the lower bound tightness decreases with the number of nodes.

Massive, heterogeneous sensors have been deployed to monitor many objects from multiple aspects, which brings the explosion of sensor data; thus, effective data collection becomes an important issue. Moreover, in emerging advanced IoT applications such as the smart city, computation tasks are released by smartphone users. Such tasks are random and unpredictable, which makes it hard to know in advance what sensor data are needed. Collecting all of the data that might be involved can lead to a huge waste of time, energy, and channel resources. In this article, we study effective sensor data collection problem and the goal is to fetch enough data to get the accurate results of random tasks with minimum cost. We propose a novel progressive computing method, where the sensor data are collected in phases. Specifically, in each collection phase, the server analyzes the data obtained in preview phases and determines what data to fetch in the next phase. Then, the server sends control messages to corresponding sensors by using the downlink communication of Lora. In our computing method, data collection is interwoven with task computing, which enables targeted approaches to the data exactly needed. Two example progressive algorithms are presented, and their performances are analyzed theoretically. Simulation results show that our algorithms can reduce the cost remarkably without loss in the accuracy of the computation results.

Thanks to the recent advances in energy-harvesting devices, nodes equipped with such devices are produced and enable Wireless Sensor Networks (WSNs) to be energy self-sustainable. Such networks are named as Battery-Free WSNs (BF-WSNs). Data aggregation is an essential operation in WSNs, and the Minimum Latency Aggregation Scheduling (MLAS) problem which seeks a collision-free aggregation scheduling with the minimum latency has been well studied in Battery-Powered WSNs (BP-WSN). In BP-WSNs, latency is mainly caused by the time overhead in collision-avoiding. However, the time-consumption for node recharging is the main cause of latency in BF-WSNs. Moreover, the collisions are time-independent while the recharge rate is time-varying. Therefore, the previous algorithms are not suitable for BF-WSNs. In addition, if aggregating data from all nodes, the latency would be determined by the node with the lowest recharge rate. Thus, we propose to aggregate a subset of nodes which can meet the given coverage quality requirement. Meanwhile, the aggregation tree and scheduling strategy should be adaptive to the current energy condition. We formulate this problem and propose a distributed algorithm which can select nodes adaptively according to their energy condition and schedule these nodes to achieve the minimum latency, simultaneously. To the best of our knowledge, it is the first distributed algorithm to solve the MLAS problem with coverage guarantee in BF-WSNs. The simulation results verify that our algorithm can reduce aggregation latency effectively, especially in bad energy condition.