Topology control is an effective method to improve the energy efficiency of wireless sensor networks (WSNs). Traditional approaches are based on the assumption that a pair of nodes is either “connected ” or “disconnected”. These approaches are called connectivity-based topology control. In real environments however, there are many intermittently connected wireless links called lossy links. Taking a succeeded lossy link as an advantage, we are able to construct more energy-efficient topologies. Towards this end, we propose a novel opportunity-based topology control. We show that opportunity-based topology control is a problem of NPhard. To address this problem in a practical way, we design a fully distributed algorithm called CONREAP based on reliability theory. We prove that CONREAP has a guaranteed performance. The worst running time is O(|E|) where E is the link set of the original topology, and the space requirement for individual nodes is O(d) where d is the node degree. To evaluate the performance of CONREAP, we design and implement a prototype system consisting of 50 Berkeley Mica2 motes. We also conducted comprehensive simulations. Experimental results show that compared with the connectivity-based topology control algorithms, CONREAP can improve the energy efficiency of a network up to 6 times. 1

We present a practical, measurement-based model that captures the effect of interference in 802.11-based wireless LAN or mesh networks. The goal is to model capacity of any given link in the presence of any given number of interferers in a deployed network, carrying any specified amount of offered load. Central to our modeling approach is a MAC-layer model for 802.11 that is fed by PHY-layer models for deferral and packet capture behaviors, which in turn are profiled based on measurements. The target network to be evaluated needs only O(N) measurement steps to gather metrics for individual links that seed the models. We provide two solution approaches – one based on direct simulation (slow, but accurate) and the other based on analytical methods (faster, but approximate). We present elaborate validation results for a 12 node 802.11b mesh network using upto 5 interfering transmissions. We demonstrate, using as comparison points three simpler modeling approaches, that the accuracy of our approach is much better, predicting link capacities with errors within 10 % of the base channel datarate for about 90% of the cases.

We investigate how to efficiently and accurately simulate wireless packet delivery. Starting from recent experimental results that have quantified signal-to-noise ratio (SNR) curves, temporal variations in propagation strength, and the effects of hardware variations, we model packet delivery using SNR. We experimentally measure noise in many different environments and propose three algorithms to simulate noise from these traces. We evaluate these algorithms in comparison to existing simulation approaches used in EmStar, TOSSIM, and ns-2 using the Kantorovich-Wasserstein distance on conditional packet delivery functions. We demonstrate that using a closest-fit pattern matching (CPM) noise model can capture complex temporal dynamics which existing approaches do not, increasing packet simulation fidelity by a factor of 2 for good links and a factor of 3 for intermediate links. Furthermore, as our models are generated from real-world traces, they are not bound to specific environments and can be easily applied to new ones. 1.

Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20-node TelosB motes testbeds – one indoor and the other outdoor – to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hop-based, range-based, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90-percentile error about 20-25 % (and 80 percentile error 7-12%), depending on the scenario. The accuracy of the other models is worse and scenario-specific. The second best model trails the physical model by roughly 12-18 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model – ‘thresholded ’ (conservative, but typically considered in literature) and ‘graded ’ (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve ‘expected throughput ’ over the thresholded version by scheduling imperfect links. Categories and Subject Descriptors C.2.1 [Network architecture and design]: Wireless communication;

Abstract—To achieve scalability, energy-efficiency, and timeliness, wireless sensor network deployments increasingly employ in-network processing. In this paper, we identify singlehop feedback collection as a key building block for in-network processing applications, and introduce a basic singlehop primitive, pollcast. The key idea behind this primitive is to exploit the receiverside collision detection information at the MAC-layer to speed-up collaborative feedback collection. Using pollcast, a node can get an affirmation about the existence of a node-level predicate P in its neighborhood in constant time by asking all nodes where P hold to reply simultaneously. We have implemented pollcast on Tmotes using Chipcon 2420 radio. Our results show that this primitive is indeed lightweight, resilient, and effective. Our paper is also the first time receiver-side collision detection is achieved in a practical manner for Chipcon 2420 radio. I.

This paper presents C-MAC, a new MAC protocol designed to achieve high-throughput bulk communication for dataintensive sensing applications. C-MAC exploits concurrent wireless channel access based on empirical power control and physical interference models. Nodes running C-MAC estimate the level of interference based on the physical Signal-to-Interference-plus-Noise-Ratio (SINR) model and adjust the transmission power accordingly for concurrent channel access. C-MAC employs a block-based communication mode that not only amortizes the overhead of channel assessment, but also improves the probability that multiple nodes within the interference range of each other can transmit concurrently. C-MAC has been implemented in TinyOS-1.x and extensively evaluated on Tmote nodes. Our experiments show that C-MAC significantly outperforms the state-of-art CSMA protocol in TinyOS with respect to system throughput, delay and energy consumption.

In wireless networks, a frame collision does not necessarily result in all the simultaneously transmitted frames being lost. Depending on the relative signal power and the arrival timing of the involved frames, one frame can survive the collision and be successfully received by the receiver. Using our IEEE 802.11a wireless network testbed, we carry out a measurement study that shows the terms and conditions (timing, power difference, bit rate) under which this capture effect takes place. A recent measurement work on the capture effect in 802.11 networks [12] argues that the stronger frame can be successfully decoded only in two cases: (1) The stronger frame arrives earlier than the weaker frame, or (2) the stronger frame arrives later than the weaker frame but within the preamble time of the weaker frame. However, our measurement shows that the stronger frame can be decoded correctly regardless of the timing relation with the weaker frame. In explaining various capture cases we observe that the successful capture of a frame involved in a collision is determined through two stages: the preamble detection and the frame check sequence (FCS) check.

Abstract—Interference modeling is crucial for the performance of numerous WSN protocols such as congestion control, link/channel scheduling, and reliable routing. In particular, understanding and mitigating interference becomes increasingly important for Wireless Sensor Networks (WSNs) as they are being deployed for many data-intensive applications such as structural health monitoring. However, previous works have widely adopted simplistic interference models that fail to capture the wireless realities such as probabilistic packet reception performance. Recent studies suggested that the physical interference model (i.e., PRR-SINR model) is significantly more accurate than existing interference models. However, existing approaches to physical interference modeling exclusively rely on the use of active measurement packets, which imposes prohibitively high overhead to bandwidth-limited WSNs. In this paper, we propose the passive interference measurement (PIM) approach to tackle the complexity of accurate physical interference characterization. PIM exploits the spatiotemporal diversity of data traffic for radio performance profiling and only needs to gather a small amount of statistics about the network. We evaluate the efficiency of PIM through extensive experiments on both a 13-node and a 40-node testbeds of TelosB motes. Our results show that PIM can achieve high accuracy of PRR-SINR modeling with significantly lower overhead compared with the active measurement approach. I.

Abstract. Wireless sensor network protocols and applications, including those used for localization, topology control, link scheduling, and link quality estimation, make extensive use of Received Signal Strength Indication (RSSI) measurements. In this paper we show that inaccuracies in the RSSI values reported by widely used 802.15.4 radios, such as the CC2420 and the AT86RF230, have profound impact on these protocols and applications. Furthermore, we experimentally derive the response curves which translate actual RSSI values to the raw RSSI readings that the radios report and show that they contain non-linear and even noninjective regions. Fortunately, these curves are consistent across radios of the same model, making RSSI calibration practical. We present a calibration mechanism that removes the artifacts in the raw RSSI measurements, including ambiguities created by the non-injective regions in the response curves, and generates calibrated RSSI readings that are linear. This calibration removes many of the outliers generated when raw RSSI readings are used to estimate Signal to Noise (and Interference) ratios, estimate radio model parameters, and perform RF-based localization. 1

Multi-channel design has received significant attention for low-power wireless networks (LWNs), such as 802.15.4-based wireless sensor networks, due to its potential of mitigating interference and improving network capacity. However, recent studies reveal that the number of orthogonal channels available on commodity wireless platforms is small, which significantly hinders the performance of existing multichannel protocols. A promising solution is to explore the use of partially overlapping channels for communications. However, this approach faces several key challenges such as increased inter-channel interference and significantly higher overhead of channel measurement. In this paper, we systematically study the inter-channel interference and its impact on link capacity and the performance of multi-channel protocols in LWNs. First, we develop empirical models for characterizing inter-channel signal attenuation based on experiments on TelosB motes. We then propose a novel measurement algorithm which can significantly reduce the overhead of multi-channel interference measurement by exploiting the spectral power density (SPD) of the transmitter. Finally, we apply our interference models to both link capacity analysis and channel assignment protocols. Our extensive experiments on a testbed of 30 TelosB motes show that our interference measurement algorithm has an average error of 2.95%. Our results also demonstrate that multi-channel protocols for LWNs can significantly benefit from using overlapping channels.