In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

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Models and issues in data stream systems

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.

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QoS-aware middleware for Web services composition

This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmission. We show that intelligently mixing packets increases network throughput. Our design is rooted in the theory of network coding. Prior work on network coding is mainly theoretical and focuses on multicast traffic. This paper aims to bridge theory with practice; it addresses the common case of unicast traffic, dynamic and potentially bursty flows, and practical issues facing the integration of network coding in the current network stack. We evaluate our design on a 20-node wireless network, and discuss the results of the first testbed deployment of wireless network coding. The results show that using COPE at the forwarding layer, without modifying routing and higher layers, increases network throughput. The gains vary from a few percent to several folds depending on the traffic pattern, congestion level, and transport protocol.

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XORs in the air: practical wireless network coding

We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.

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TinyDB: an acquisitional query processing system for sensor networks

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is ‘poor’, analysis and experimentation in prior work has indicated that TCP clocks itself quite well, avoiding contention-driven WiFi overload in dense settings. This paper focuses on measurements from a real-life use of WiFi in a dense scenario: a classroom where several students use the network to download quizzes and instruction material. We find that the TCP download performance is poor, contrary to that suggested by prior work. Through careful analysis, we explain the complex interaction of various phenomena which leads to this poor performance. Specifically, we observe that a small amount of upload traffic generated when downloading data upsets the TCP clocking, and increases contention on the channel. Further, contention losses lead to a vicious cycle of poor interaction with autorate adaptation and TCP's timeout mechanism. To reduce channel contention and improve performance, we propose a modification to the AP scheduling policy to improve the performance of large TCP downloads. Our solution, WiFiRR, picks only a subset of clients to be served by the AP during any instant, and varies this set of “active” clients periodically in a round-robin fashion over all clients to ensure that no client starves. By reducing the number of contending nodes at any point of time, WiFiRR improves the download time of large TCP flows by 3.2× in a simulation of our classroom scenario.

TCP download performance in dense WiFi scenarios

Commuting on roads in densely populated cities of the developing world is fraught with high delays and uncertainties. Wide use of public transportation can ease the load on the road infrastructure, but such use is not convenient, partly due to the unpredictable nature. In this work, our goal is to improve the usability of public transportation, through better information. Such information can lead to better planning and predictability for commuters. We take a crowd-sourced approach where information about transportation units as well as road conditions is crowd-sourced from commuters. The information is then processed and made available to other commuters. In this context, this paper presents a naming framework we have developed, which will enable flexible and scalable content-driven data gathering and dissemination. Based on a preliminary implementation of the framework, we present various field-experiment results which shed light on the practicality of the proposed approach as well as on technical issues which need further careful addressing.

Improving public transportation through crowd-sourcing

In this article, we consider the goal of achieving high throughput in a wireless sensor network. Our work is set in the context of those wireless sensor network applications which collect and transfer bulk data. We present PIP (Packets in Pipe), a MAC primitive for use by the transport module to achieve high throughput. PIP has a unique set of features: (a) it is a multihop connection-oriented primitive, (b) it is TDMA based, (c) it uses multiple radio channels, and (d) it is centrally controlled. This represents a significant shift from prior MAC protocols for bulk data transfer. PIP has several desirable properties: (a) its throughput degrades only slightly with increasing number of hops, (b) it is robust to variable wireless error rates, (c) it performs well even without any flow control, and (d) requires only small queue sizes to operate well. We substantiate these properties with a prototype implementation of PIP on the Tmote-Sky CC2420-based platform. PIP achieves about eleven times better throughput than the state-of-the-art prior work, over a network depth of 24 hops. We also show that PIP can be interagted with duty cycling, and that PIP can support streaming data from/to flash at little overhead.

PIP: A multichannel, TDMA-based MAC for efficient and scalable bulk transfer in sensor networks

Frame aggregation improves performance of 802.11 networks by eliminating many overheads the single-frame transmissions incur. An originator builds and sends an aggregate (A-MPDU) of packets (MPDUs or subframes) from a window of 64 sequence numbers, and a block ACK then acknowledges the same window. The mechanism works fine till there are no packet losses. With the advent of packet losses (and their subsequent reporting by the block ACK), holes appear in the originator's aggregation window. An A-MPDU constructed immediately after by the originator then becomes smaller in size due to these holes. We discuss the effect of such inadvertent shortening of transmitted A-MPDUs on the performance of WLAN. We propose modifications to the block ACK and aggregation mechanisms, and show through our ns-3 simulations that the change improves the network throughput by up to 20% in hidden node scenario in simple basic service sets (BSSs) consisting of 32 to 48 STAs. Our analysis shows that improvement in the worst case throughput can be as high as 400% for the highest VHT MCS indexes with our proposed modifications.

802.11ac Frame Aggregation is Bottlenecked: Revisiting the Block ACK

Cost optimization is an important criterion in technology deployment for developing regions. While IEEE 802.11-based long-distance networks may provide a cost-effective option to connect remote villages, planning such a network to cover the villages in a given region can be a non-trivial task. This is especially so since the antenna tower cost is the dominant cost in such scenarios. In this thesis, we consider the problem of topology planning in the context of 802.11-based long-distance rural networks. Our first contribution is the formulation of this problem in terms of its constraints and the optimization metric. We find the problem to be of combinatorial nature, and hence not scalable for large input sets. Our next contribution is in breaking down the problem into its constituent sub-parts some of which are independent, this makes the solution procedure tractable. Next we propose several searching and pruning strategies in generation of an optimal solution. We then present a set of results generated by the algorithm. We have been able to find topologies for sets of nodes of size 31 (so far). For a sub-set of the Ashwini (31 out of 34 nodes) project, a long distance rural network currently being deployed in the West Godavari district of Andhra Pradesh we have been able to show a potential cost benefit of the order of 15 times on their tower cost budget (based on available data for already commissioned towers), by improving the process of assigning tower heights. Also careful topology planning led to a further 22 % savings over their current deployment plan. Another fact to be borne in mind is the current deployment utilizes three independent WiFi channel while our solution only uses one channel. A final contribution of this thesis is the discussion of further work necessary in this domain.

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Bhaskaran Raman

Papers

TCP download performance in dense WiFi scenarios

Improving public transportation through crowd-sourcing

PIP: A multichannel, TDMA-based MAC for efficient and scalable bulk transfer in sensor networks

802.11ac Frame Aggregation is Bottlenecked: Revisiting the Block ACK

Topology Planning for Long Distance Wireless Mesh Networks