Yanjun Yao

EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks

Our work in this paper stems from our insight that recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. Therefore, it may be feasible that we could adapt these techniques in such a way that they will provide valuable solutions to certain tricky problems in the wireless sensor network (WSN) domain. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL leverages one result from OVR to prove that the problem formulation is inherently NP-hard. Therefore, we proposed both a centralized heuristic to reduce its computational overhead and a distributed heuristic to make the algorithm scalable for large-scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to compare its performance to related protocols in both simulations and a hardware testbed.

EDAL: An Energy-Efficient, Delay-Aware, and Lifetime-Balancing Data Collection Protocol for Wireless Sensor Networks

In many wireless sensor network (WSN) applications, a subset of nodes (source nodes) are selected to sense the environment, generate data, and transmit them back to the sink over multiple hops. Many previous research efforts have tried to achieve trade-offs in terms of delay, energy cost, and load balancing for such data collection tasks. Our work in this paper stems from the insight that, recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. This insight motivates us to adapt these techniques so that we can solve or prove certain challenging problems in WSN applications. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL borrows one research result from OVR to prove that its problem formulation is inherently NP-hard. We then proposed both a centralized heuristic to reduce its computational overhead, and a distributed heuristic to make the algorithm scalable for large scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to demonstrate its performance superiority compared to related protocols.

CARPO: Correlation-aware power optimization in data center networks

Power optimization has become a key challenge in the design of large-scale enterprise data centers. Existing research efforts focus mainly on computer servers to lower their energy consumption, while only few studies have tried to address the energy consumption of data center networks (DCNs), which can account for 20% of the total energy consumption of a data center. In this paper, we propose CARPO, a correlation-aware power optimization algorithm that dynamically consolidates traffic flows onto a small set of links and switches in a DCN and then shuts down unused network devices for energy savings. In sharp contrast to existing work, CARPO is designed based on a key observation from the analysis of real DCN traces that the bandwidth demands of different flows do not peak at exactly the same time. As a result, if the correlations among flows are considered in consolidation, more energy savings can be achieved. In addition, CARPO integrates traffic consolidation with link rate adaptation for maximized energy savings. We implement CARPO on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 8 servers. Our empirical results with Wikipedia traces demonstrate that CARPO can save up to 46% of network energy for a DCN, while having only negligible delay increases. CARPO also outperforms two state-of-the-art baselines by 19.6% and 95% on energy savings, respectively. Our simulation results with 61 flows also show the superior energy efficiency of CARPO over the baselines.

Dynamic duty cycle control for end-to-end delay guarantees in wireless sensor networks

It is well known that periodically putting nodes into sleep can effectively save energy in wireless sensor networks, at the cost of increased communication delays. However, most existing work mainly focuses on static sleep scheduling, which cannot guarantee the desired delay when the network conditions change dynamically. In many applications with user-specified end-to-end delay requirements, the duty cycle of every node should be tuned individually at runtime based on the network conditions to achieve the desired end-to-end delay guarantees and energy efficiency. In this paper, we propose DutyCon, a control theory-based dynamic duty cycle control approach. DutyCon decomposes the end-to-end delay guarantee problem into a set of single-hop delay guarantee problems along each data flow in the network. We then formulate the single-hop delay guarantee problem as a dynamic feedback control problem and design the controller rigorously, based on feedback control theory, for analytic assurance of control accuracy and system stability. DutyCon also features a queuing delay adaptation scheme that adapts the duty cycle of each node to unpredictable packet rates, as well as a novel energy balancing approach that extends the network lifetime by dynamically adjusting the delay requirement allocated to each hop. Our empirical results on a hardware testbed demonstrate that DutyCon can effectively achieve the desired tradeoff between end-to-end delay and energy conservation. Extensive simulation results also show that DutyCon outperforms two baseline sleep scheduling protocols by having more energy savings while meeting the end-to-end delay requirements.

Correlation-Aware Traffic Consolidation for Power Optimization of Data Center Networks

Power optimization has become a key challenge in the design of large-scale enterprise data centers. Existing research efforts focus mainly on computer servers to lower their energy consumption, while only few studies have tried to address data center networks (DCNs), which can account for 10-20 percent of the total energy consumption of a data center. In this paper, we propose CARPO, a correlation-aware power optimization algorithm that dynamically consolidates traffic flows onto a small set of links and switches in a DCN and then shuts down unused network devices for energy savings. In sharp contrast to existing work, CARPO is designed based on a key observation from the analysis of real DCN traces that the bandwidth demands of different flows do not peak at exactly the same time. As a result, if the correlations among flows are considered in consolidation, more energy savings can be achieved. In addition, CARPO integrates traffic consolidation with link rate adaptation for maximized energy savings. We implement CARPO on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 8 servers. Our empirical results with traces from Wikipedia and Yahoo! data centers demonstrate that CARPO can save up to 50 percent of network energy for a DCN, while having only negligible delay increases. CARPO also outperforms two state-of-the-art baselines by 19.6 and 95 percent on energy savings, respectively. Our simulation results with a large-scale DCN also show that CARPO can achieve more energy savings than the baselines for typical DCN topologies, such as fat tree and BCube.

Exploiting overlapping channels for minimum power configuration in real-time sensor networks

Multi-channel communications can effectively reduce channel competition and interferences in a wireless sensor network, and thus achieve increased throughput and improved end-to-end delay guarantees with reduced power consumption. However, existing work relies only on a small number of orthogonal channels, resulting in degraded performance when a large number of data flows need to be transmitted on different channels. In this paper, we conduct empirical studies to investigate the interferences among overlapping channels. Our results show that overlapping channels can also be utilized for improved real-time performance if the node transmission power is carefully configured. In order to minimize the overall power consumption of a network with multiple data flows under end-to-end delay constraints, we formulate a constrained optimization problem to configure the transmission power level for every node and assign overlapping channels to different data flows. Since the optimization problem has an exponential computational complexity, we then present a heuristic algorithm designed based on Simulated Annealing to find a suboptimal solution. Our empirical results on a 25-mote testbed demonstrate that our algorithm achieves better real-time performance and less power consumption than two baselines including a scheme using only orthogonal channels.

kBF: A Bloom Filter for key-value storage with an application on approximate state machines

Key-value (k-v) storage has been used as a crucial component for many network applications, such as social networks, online retailing, and cloud computing. Such storage usually provides support for operations on key-value pairs, and can be stored in memory to speed up responses to queries. So far, existing methods have been deterministic: they will faithfully return previously inserted key-value pairs. Providing such accuracy, however, comes at the cost of memory and CPU time. In contrast, in this paper, we present an approximate k-v storage that is more compact than existing methods. The tradeoff is that it may, theoretically, return a null value for a valid key with a low probability, or return a valid value for a key that was never inserted. Its design is based on the probabilistic data structure called the “Bloom Filter”, which was originally developed to test element membership in sets. In this paper, we extend the bloom filter concept to support key-value operations, and demonstrate that it still retains the compact nature of the original bloom filter. We call the resulting design as the kBF (key-value bloom filter), and systematically analyze its performance advantages and design tradeoffs. Finally, we apply the kBF to a practical problem of implementing a state machine in network intrusion detection to demonstrate how the kBF can be used as a building block for more complicated software infrastructures.

Efficient Histogram Estimation for Smart Grid Data Processing With the Loglog-Bloom-Filter

With the emerging area of smart grids, one critical challenge faced by administrators of wide-area measurement systems is to analyze and model streaming data with limited resources on their embedded controllers. Usually, streaming data can be modeled as a multiset where each data item has its own frequency. In this paper, we study the problem on how to generate histograms of data items based on their frequency, so we can identify various issues such as power line tripping or line faults under constraints. The primary challenge for achieving this goal using conventional methods is that keeping an individual counter for each unique type of data is too memory-consuming, slow, and costly. In this paper, we describe a novel data structure and its associated algorithms, called the loglog bloom filter, for this purpose. This data structure extends the classical bloom filter with a recent technique called probabilistic counting, so it can effectively generate histograms for streaming data in one pass with sub-linear overhead. Therefore, this method is suitable for data processing in smart grids, where limited computational resources are available on the controllers. We analyze the performance, trade-offs, and capacity of this data structure, and evaluate it with real data traces collected through the frequency disturbance recorders deployed for the FNET/GridEye infrastructure. We demonstrate that this method can identify the frequencies of all unique items with high accuracy and low memory overhead, so that data outliers can be conveniently identified.

System Architecture and Operating Systems

The emergence of resource constrained embedded systems such as sensor networks have introduced unique challenges for the design and implementation of operating systems. In OS designs for these systems, only partial functionality is required compared to conventional ones, as their code is running on a much more restricted and homogeneous platform. In fact, as illustrated by microcontrollers, most hardware platforms in wireless sensor networks (WSNs) simply do not have the required resources to support a full-fledged operating system. Instead, operating systems for WSNs should adapt to their unique properties, which motivate the design and development of a range of unique operating systems for WSNs in recent years. In this chapter, we systematically survey these operating systems, compare them in their unique designs, and provide our insights on their strengths and weaknesses. We hope that such an approach is helpful for the reader to get a clear view of recent developments of wireless sensor network operating systems.

kBF: Towards Approximate and Bloom Filter based Key-Value Storage for Cloud Computing Systems

As one of the most popular cloud services, data storage has attracted great attention in recent research efforts. Key-value (k-v) stores have emerged as a popular option for storing and querying billions of key-value pairs. So far, existing methods have been deterministic. Providing such accuracy, however, comes at the cost of memory and CPU time. In contrast, we present an approximate k-v storage for cloud-based systems that is more compact than existing methods. The tradeoff is that it may, theoretically, return errors. Its design is based on the probabilistic data structure called “bloom filter”, where we extend the classical bloom filter to support key-value operations. We call the resulting design as the kBF (key-value bloom filter). We further develop a distributed version of the kBF (d-kBF) for the unique requirements of cloud computing platforms, where multiple servers cooperate to handle a large volume of queries in a load-balancing manner. Finally, we apply the kBF to a practical problem of implementing a state machine to demonstrate how the kBF can be used as a building block for more complicated software infrastructures.