This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks Wireless sensor networks use battery-operated computing and sensing devices A network of these devices will collaborate for a common application such as environmental monitoring We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 80211 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important S-MAC uses three novel techniques to reduce energy consumption and support self-configuration To reduce energy consumption in listening to an idle channel, nodes periodically sleep Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules Inspired by PAMAS, S-MAC also sets the radio to sleep during transmissions of other nodes Unlike PAMAS, it only uses in-channel signaling Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley The experiment results show that, on a source node, an 80211-like MAC consumes 2-6 times more energy than S-MAC for traffic load with messages sent every 1-10 s

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An energy-efficient MAC protocol for wireless sensor networks

In this paper we describe T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an activesleep duty cycle. To handle load variations in time and location T-MAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a reasonable throughput.We discuss the design of T-MAC, and provide a head-to-head comparison with classic CSMA (no duty cycle) and S-MAC (fixed duty cycle) through extensive simulations. Under homogeneous load, T-MAC and S-MAC achieve similar reductions in energy consumption (up to 98%) compared to CSMA. In a sample scenario with variable load, however, T-MAC outperforms S-MAC by a factor of 5. Preliminary energy-consumption measurements provide insight into the internal workings of the T-MAC protocol.

/pdf/an-adaptive-energy-efficient-mac-protocol-for-wireless-12upticrf7.pdf

An adaptive energy-efficient MAC protocol for wireless sensor networks

This paper proposes S-MAC, a medium access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with nodes remaining largely inactive for long time, but becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in several ways: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses a few novel techniques to reduce energy consumption and support self-configuration. It enables low-duty-cycle operation in a multihop network. Nodes form virtual clusters based on common sleep schedules to reduce control overhead and enable traffic-adaptive wake-up. S-MAC uses in-channel signaling to avoid overhearing unnecessary traffic. Finally, S-MAC applies message passing to reduce contention latency for applications that require in-network data processing. The paper presents measurement results of S-MAC performance on a sample sensor node, the UC Berkeley Mote, and reveals fundamental tradeoffs on energy, latency and throughput. Results show that S-MAC obtains significant energy savings compared with an 802.11-like MAC without sleeping.

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Medium access control with coordinated adaptive sleeping for wireless sensor networks

We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing fidelity GAF moderates this policy using application- and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol Moreover, simulations of GAP suggest that network lifetime increases proportionally to node density; in one example, a four-fold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern) More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of self-configuring systems by exploiting redundancy to conserve energy while maintaining application fidelity

/pdf/geography-informed-energy-conservation-for-ad-hoc-routing-2r8tnehjy4.pdf

Geography-informed energy conservation for Ad Hoc routing

We present a new metric for routing in multi-radio, multi-hop wireless networks. We focus on wireless networks with stationary nodes, such as community wireless networks.The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The ETT is a function of the loss rate and the bandwidth of the link. The individual link weights are combined into a path metric called Weighted Cumulative ETT (WCETT) that explicitly accounts for the interference among links that use the same channel. The WCETT metric is incorporated into a routing protocol that we call Multi-Radio Link-Quality Source Routing.We studied the performance of our metric by implementing it in a wireless testbed consisting of 23 nodes, each equipped with two 802.11 wireless cards. We find that in a multi-radio environment, our metric significantly outperforms previously-proposed routing metrics by making judicious use of the second radio.

/pdf/routing-in-multi-radio-multi-hop-wireless-mesh-networks-pnd82vmzoa.pdf

Routing in multi-radio, multi-hop wireless mesh networks

No single wireless network technology simultaneously provides a low latency, high bandwidth, wide area data service to a large number of mobile users. Wireless Overlay Networks – a hierarchical structure of room-size, building-size, and wide area data networks – solve the problem of providing network connectivity to a large number of mobile users in an efficient and scalable way. The specific topology of cells and the wide variety of network technologies that comprise wireless overlay networks present new problems that have not been encountered in previous cellular handoff systems. We have implemented a vertical handoff system that allows users to roam between cells in wireless overlay networks. Our goal is to provide a user with the best possible connectivity for as long as possible with a minimum of disruption during handoff. Results of our initial implementation show that the handoff latency is bounded by the discovery time, the amount of time before the mobile host discovers that it has moved into or out of a new wireless overlay. This discovery time is measured in seconds: large enough to disrupt reliable transport protocols such as TCP and introduce significant disruptions in continuous multimedia transmission. To efficiently support applications that cannot tolerate these disruptions, we present enhancements to the basic scheme that significantly reduce the discovery time without assuming any knowledge about specific channel characteristics. For handoffs between room-size and building-size overlays, these enhancements lead to a best-case handoff latency of approximately 170 ms with a 1.5% overhead in terms of network resources. For handoffs between building-size and wide-area data networks, the best-case handoff latency is approximately 800 ms with a similarly low overhead.

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Vertical handoffs in wireless overlay networks

Measuring and Reducing Energy Consumption of Network Interfaces in Hand-Held Devices

In the Internet today, users and applications must often make decisions based on the performance they expect to receive from other Internet hosts. For example, users can often view many Web pages in low-bandwidth or high-bandwidth versions, while other pages present users with long lists of mirror sites to chose from. Current techniques to perform these decisions are often ad hoc or poorly designed. The most common solution used today is to require the user to manually make decisions based on their own experience and whatever information is provided by the application. Previous efforts to automate this decision-making process have relied on isolated, active network probes from a host. Unfortunately, this method of making measurements has several problems. Active probing introduces unnecessary network traffic that can quickly become a significant part of the total traffic handled by busy Web servers. Probing from a single host results in less accurate information and more redundant network probes than a system that shares information with nearby hosts. In this paper, we propose a system called SPAND (Shared Passive Network Performance Discovery) that determines network characteristics by making shared, passive measurements from a collection of hosts. In this paper, we show why using passive measurements from a collection of hosts has advantages over using active measurements from a single host. We also show that sharing measurements can significantly increase the accuracy and timeliness of predictions. In addition, we present a initial prototype design of SPAND, the current implementation status of our system, and initial performance results that show the potential benefits of SPAND.

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SPAND: shared passive network performance discovery

The rapid growth of the World Wide Web in recent years has caused a significant shift in the composition of Internet traffic. Although past work has studied the behavior of TCP dynamics in the context of bulk-transfer applications and some studies have begun to investigate the interactions of TCP and HTTP, few have used extensive real-world traffic traces to examine the way in which TCP is used by current Web browsers. This interaction is interesting because of the way in which current Web browsers use TCP connections: multiple parallel short connections from a single host. In this paper, we analyze the way in which Web browsers use TCP connections based on extensive traffic traces obtained from a busy Web server (the official Web server of the 1996 Atlanta Olympic games). At the time of operation, this Web server was one of the busiest on the Internet, handling tens of millions of requests per day from hundreds of thousands of clients. We first describe the techniques used to gather these traces and reconstruct the behavior of TCP on the server. We then present a detailed analysis of TCPs loss recovery and congestion control behavior from the recorded transfers. The two most important results are that: (1) short Web transfers lead to poor loss recovery performance for TCP, and (2) concurrent connections cause an overly aggressive use of the network. We then discuss techniques designed to solve these problems. To improve the loss recovery performance of short transfers, we present a new technique for TCP loss recovery. To improve the congestion control and loss recovery performance of multiple parallel connections, we present a new transport-level integrated connection approach to congestion control and loss recovery. Simulation and trace analysis results show that our enhanced loss recovery scheme could have eliminated 25% of all timeout events, and our integrated connection approach provides greater fairness and improved startup performance for parallel connections. Our solutions are more general than a specific protocol enhancement such as the use of persistent connections in P-HTTP [15] and HTTP/1.1 [6], and include techniques, such as improved TCP loss recovery, that are not addressed by these protocols.

TCP Behavior of a Busy Internet Server: Analysis and Improvements

We analyze the way in which Web browsers use TCP connections based on extensive traffic traces obtained from a busy Web server (the official Web server of the 1996 Atlanta Olympic games). At the time of operation, this Web server was one of the busiest on the Internet. We first describe the techniques used to gather these traces and reconstruct the behavior of the TCP on the server. We then present a detailed analysis of the TCP's loss recovery and congestion control behavior from the recorded transfers. Our two most important results are: (1) short Web transfers lead to poor loss recovery performance for TCPs, and (2) concurrent connections are overly aggressive users of the network. We then discuss techniques designed to solve these problems. To improve the data-driven loss recovery performance of short transfers, we present a new enhancement to the TCP's loss recovery. To improve the congestion control and loss recovery performance of parallel TCP connections, we present a new integrated approach to congestion control and loss recovery that works across the set of concurrent connections. Simulations and trace analysis show that our enhanced loss recovery scheme could have eliminated 25% of all timeout events, and that our integrated approach provides greater fairness and improved startup performance for concurrent connections.

/pdf/tcp-behavior-of-a-busy-internet-server-analysis-and-75r8h23to6.pdf

Mark Stemm

Papers

Vertical handoffs in wireless overlay networks

Measuring and Reducing Energy Consumption of Network Interfaces in Hand-Held Devices

SPAND: shared passive network performance discovery

TCP Behavior of a Busy Internet Server: Analysis and Improvements

TCP behavior of a busy Internet server: analysis and improvements