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Understanding and Improving the Performance of Constructive Interference Using Destructive Interference in WSNs
Abstract
The constructive interference (CI) phenomenon has been exploited by a number of protocols for providing energy-efficient, low-latency, and reliable data collection and dissemination services in wireless sensor networks. These protocols consider CI to provide highly reliable packet delivery. This has attracted attention to understand the working of CI; however, the existing works present inconsistent views. Furthermore, these works do not study in the real-world settings where the physical conditions of deployment and unreliable wireless channels also impact the performance of CI. Therefore, we study the phenomenon of CI, considering a receiver's viewpoint and analyze the parameters that affect CI. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into the CI phenomenon. With the understanding, we develop the destructive interference-based power adaptation (DIPA), an energy-efficient and distributed algorithm, that adapts transmission power to improve the performance of CI. Since CI-based protocols cannot have an explicit acknowledgment packet, we make use of destructive interference on a designated byte to provide a feedback. We leverage this feedback to adapt transmission powers. We compared CI with and without DIPA in two real-life testbeds. On one testbed, we achieve around 25% lower packet losses while using only half of its transmission power for 64-B packets. On the other testbed, we achieve 25% lower packet losses while consuming only 47% of its transmission power for 128-B packets. Existing CI-based protocols can easily incorporate DIPA into them to achieve lower packet losses and higher energy efficiencies.
... In a MAC scenario, the bandwidth requirement no longer depends on the number of sensors. However, DF in MAC is corrupted with intrinsic interference resulting from intersensor-element interference (ISEI) and inter-sensor-channel interference (ISCI) [2]. Interference caused by partial overlap of multiple sensor signals in time results in ISEI, while ISCI is caused by the superposition of sensor signals when sent over a MAC. ...
... The noise vector also accounts for different levels of channel state information (CSI) estimation error, where the estimated channel on the receiver side is contaminated by additive Gaussian noise.2 (·) t denotes transpose, R(·), E{·}, V{·}, ∠(·), (·) † , || · ||, p(·) and P (·) represents real-part, expectation, variance, phase, conjugate transpose, Frobenius norm operators, probability density function and probability mass function respectively; N (µ, Σ) denote normal distribution with mean µ and co-variance matrix Σ.3 The DFC estimates the CSI, where half of the coherence interval of 2T is used for training to estimate the channel and establish the frequency and timing synchronization. ...
In this letter, we consider the plaguing, yet rarely handled problem of interference resulting from superposition of multiple sensor signals in time, when sent over a multiple access channel (MAC) in wireless sensor networks (WSNs). We propose space-time spreading (STS) of local sensor decisions before reporting them over a MAC to i) minimize interference and ii) reduce energy required for combating interference due to superposition of sensor decisions. Each sensor decision is encoded on appropriately indexed space-time block of fixed duration using dispersion vectors, such that a single sensor is activated over each space-time block while all the other sensors are silent. At the receive side of the reporting channel, we assume a multi-antenna decision fusion center (DFC), thereby representing a distributed multiple-input-multiple-output (MIMO) communication scenario. We formulate and compare optimum and sub-optimum fusion rules for fusing sensor decisions at the DFC to arrive at a reliable conclusion. Simulation results demonstrate gain in fusion performance with STS-aided transmission by 3 to 6 times over performance without STS.
... Nevertheless, CI-based flooding has scalability problems. Incresing transmitting nodes makes it harder to keep precise timing across several synchronous transmitters [20], which compromises the reliability of concurrent transmission. It's also difficult to achieve comparable data transfer capacity because CI needs multiple • Firstly, we investigated CI-based data collection, proposed a novel data collection protocol based on network flooding communication, and designed the entire protocol's process. ...
This paper presents a cluster-based data collection protocol for wireless sensor networks. Constructive interference-based flooding presents a novelway to achieve global synchronization, yet its communication mechanism requires that each message’s transmission involve a flooding process across a whole network, which suffers from scalability problems, especially for a densely deployed network. Additionally, an increase in the number of transmitting nodes compromises concurrent transmission reliability. This work aims to tackle these problems from a clustering perspective. Firstly, we improved the time synchronization method proposed by constructive inference-based flooding to make it applicable to our protocol. Then, we designed a heuristic algorithm from the balance and connectivity viewpoints to carry out network clustering simultaneously during the data collection process. The prototype was implemented on Tmote Sky sensor nodes. Extensive simulations using the Contiki operating system and experiments on a deployed testbed have verified that the protocol can achieve almost 100% data collection with lower energy consumption.
... Finally, interference refers to the occurrence of overlapping transmissions in the network that can cause interference and affects the network performance. Interference can occur due to various factors, such as the active nodes, transmission power, and channel bandwidth [6][7]. In WSNs, minimizing interference is crucial to ensuring the accuracy and reliability of data transmission. ...
... Flooding activities from the sink node to source nodes pave the way of downward packet transportation, which is able to piggyback negative ACK and achieve end-to-end packet retransmission. Constructive interference based flooding is also able to provide global synchronization in an implicit way [18]. ...
This paper presents a robust data gathering protocol for wireless sensor network applications that require high throughput, reliability, and low-power. Insofar as high throughput is concerned, TDMA is more suitable than CSMA. Efficient TDMA protocols require solid synchronization and more optimized time slot scheduling. Reliable data collection calls for end-to-end packet retransmission. Such factors hinder the implementation of a comprehensive data collection protocol. In this paper, a heuristic TDMA scheduling algorithm is provided to achieve optimized time slot scheduling with multi-channel communication. The combination of TDMA and multi-channel communication affords to sustain high throughput and low-power communication. In addition, this paper leverages constructive interference based flooding to provide robust synchronization and efficient downward communication. Therefore, we are able to associate constructive interference based flooding with packet retransmission to implement collection reliability. The proposed protocol is implemented on self-made sensor node with Contiki operating system. Experimental results in a deployed network consisting of 29 self-made sensor nodes show that the protocol is able to maintain lasting operation and provide throughput as high as 14.74 KByte/s with 100\% data collection.
... of devices. In such a scenario, all devices communicate simultaneously over a shared spectrum and multi-access channel (MAC) [2]. The gateway (or decision-making center) receives multiple data-streams superposed in time. ...
In this article, we propose a space spreading-assisted framework that leverages either time or frequency diversity or both to reduce interference and signal loss owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.
... That is no longer a solution with hundreds of devices coexisting in a dense IoT network, as the bandwidth requirement increases exponentially with the increase in the number of devices. In such a scenario, all devices communicate simultaneously over a shared spectrum and multi-access channel (MAC) [2]. The gateway (or decision-making center) receives multiple data-streams superposed in time. ...
In this article, we propose a space spreading-assisted framework that leverages either time or frequency diversity or both to reduce interference and signal loss owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.
... Transmitted signals are obstructed and scattered, corrupted by noise and distorted by intrinsic interference on their way to the IoT gateway or the receiving devices. Noise can be contributed by the environment, internal electronics of the devices or frequency-dependent anomalies [1], and interference is caused by the superposition of the transmitted signals when sent simultaneously over the multiple access channel [2]. ...
div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div
... Transmitted signals are obstructed and scattered, corrupted by noise and distorted by intrinsic interference on their way to the IoT gateway or the receiving devices. Noise can be contributed by the environment, internal electronics of the devices or frequency-dependent anomalies [1], and interference is caused by the superposition of the transmitted signals when sent simultaneously over the multiple access channel [2]. ...
div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div
A robust data collection protocol is proposed in this paper for wireless sensor networks (WSNs) applications which require high throughput, reliability and low-power. In terms of high throughput, time division multiple access (TDMA) is more suitable than carrier sense multiple access (CSMA). Efficient TDMA protocol requires solid synchronization and more optimized time slot scheduling. Reliable data collection requires end-to-end packet retransmission. These factors inhibit the implementation of comprehensive data collection protocols. This paper provides a heuristic TDMA scheduling algorithm for optimal time slot scheduling of multi-channel communication. The combination of TDMA and multi-channel communication enables to maintain high throughput and low power communication. Besides, constructive interference (CI) based flooding technique is utilized in this paper to deliver robust synchronization and efficient downward communication. Thus, we are able to combine constructive interference based flooding with packet retransmission for collection reliability. The proposed protocol is implemented on self-made sensor nodes through the Contiki operating system. Experimental results from a deployed network of 29 self-made sensor nodes show that the protocol is able to sustain persistent operation and provide a throughput of up to 14.74 KByte/s with 100% data collection.
As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Since ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of
and more than 80% packet reception rate, which outperforms state-of-the-art mZig.
Low-power wireless bus (LWB) is a highly efficient communication protocol for wireless sensor networks (WSN). A lack of open implementation for this protocol prompted the implementation of the protocol. This document provides the description of the implementation. This implementation is targeted for the most common platform of WSN related research, the 'sky' motes. The implementation is done using the Contiki operating system. A forwarder-selection mechanism is developed to improve energy-efficiency of LWB in data collection scenario. The implementation also consists the forwarder selection mechanism. One can either use the original features of LWB or enforce the forwarder selection mechanism.
During phases of transient connectivity, sensor nodes receive a substantial number of corrupt packets. These corrupt packets are generally discarded, losing the sent information and wasting the energy put into transmitting and receiving. Our analysis of one year's data from an outdoor sensor network deployment shows that packet corruption follows a distinct pattern that is observed on all links. We explain the pattern's core features by considering implementation aspects of low-cost 802.15.4 transceivers and independent transmission errors. Based on the insight into the corruption pattern, we propose a probabilistic approach to recover information about the original content of a corrupt packet. Our approach vastly reduces the uncertainty about the original content, as measured by a manifold reduction in entropy. We conclude that the practice of discarding all corrupt packets in an outdoor sensor network may be unnecessarily wasteful, given that a considerable amount of information can be extracted from them.
Constructive baseband interference has been recently introduced in low-power wireless networks as a promising technique enabling low-latency network flooding and sub-μs time synchronisation among network nodes. The scalability of this technique has been questioned in regards to the maximum temporal misalignment among baseband signals, due to the variety of path delays in the network. By contrast, we find that the scalability is compromised, in the first place, by emerging fast fading in the composite channel, which originates in the carrier frequency disparity of the participating repeaters nodes. We investigate the multisource wave problem and show the resulting signal becomes vulnerable in the presence of noise, leading to significant deterioration of the link whenever the carriers have similar amplitudes.
Numerous studies have shown that concurrent transmissions can help to boost wireless network performance despite the possibility of packet collisions. However, while these works provide empirical evidence that concurrent transmissions may be received reliably, existing signal capture models only partially explain the root causes of this phenomenon. We present a comprehensive mathematical model for MSK-modulated signals
that makes the reasons explicit and thus provides fundamental insights on the key parameters governing the successful reception of colliding transmissions. A major contribution is the closed-form derivation of the receiver bit decision variable for an arbitrary number of colliding signals and constellations of power ratios, time offsets, and carrier phase offsets. We systematically explore the factors for successful packet delivery under concurrent transmissions across the whole parameter space of the model. We confirm the capture threshold behavior observed in previous studies but also reveal new insights relevant to the design of optimal protocols: We identify capture zones depending not only on the signal power ratio but also on time and phase offsets.
We present the Low-Power Wireless Bus (LWB), a communication protocol that supports several traffic patterns and mobile nodes immersed in static infrastructures. LWB turns a multi-hop low-power wireless network into an infrastructure similar to a shared bus, where all nodes are potential receivers of all data. It achieves this by mapping all traffic demands on fast network floods, and by globally scheduling every flood. As a result, LWB inherently supports one-to-many, many-to-one, and many-to-many traffic. LWB also keeps no topology-dependent state, making it more resilient to link changes due to interference, node failures, and mobility than prior approaches. We compare the same LWB prototype on four testbeds with seven state-of-the-art protocols and show that: (i) LWB performs comparably or significantly better in many-to-one scenarios, and adapts efficiently to varying traffic loads; (ii) LWB outperforms our baselines in many-to-many scenarios, at times by orders of magnitude; (iii) external interference and node failures affect LWB's performance only marginally; (iv) LWB supports mobile nodes acting as sources, sinks, or both without performance loss.
It is generally considered that concurrent transmissions should be avoided in
order to reduce collisions in wireless sensor networks. Constructive
interference (CI) envisions concurrent transmissions to positively interfere at
the receiver. CI potentially allows orders of magnitude reductions in energy
consumptions and improvements on link quality. In this paper, we theoretically
introduce a sufficient condition to construct CI with IEEE 802.15.4 radio for
the first time. Moreover, we propose Triggercast, a distributed middleware, and
show it is feasible to generate CI in TMote Sky sensor nodes. To synchronize
transmissions of multiple senders at the chip level, Triggercast effectively
compensates propagation and radio processing delays, and has
percentile synchronization errors of at most 250ns. Triggercast also
intelligently decides which co-senders to participate in simultaneous
transmissions, and aligns their transmission time to maximize the overall link
PRR, under the condition of maximal system robustness. Extensive experiments in
real testbeds reveal that Triggercast significantly improves PRR from 5% to 70%
with 7 concurrent senders. We also demonstrate that Triggercast provides on
average PRR performance gains, when integrated with existing data
forwarding protocols.
This paper presents Glossy, a novel flooding architecture for wireless sensor networks. Glossy exploits constructive interference of IEEE 802.15.4 symbols for fast network flooding and implicit time synchronization. We derive a timing requirement to make concurrent transmissions of the same packet interfere constructively, allowing a receiver to decode the packet even in the absence of capture effects. To satisfy this requirement, our design temporally decouples flooding from other network activities. We analyze Glossy using a mixture of statistical and worst-case models, and evaluate it through experiments under controlled settings and on three wireless sensor testbeds. Our results show that Glossy floods packets within a few milliseconds and achieves an average time synchronization error below one microsecond. In most cases, a node receives the flooding packet with a probability higher than 99.99%, while having its radio turned on for only a few milliseconds during a flood. Moreover, unlike existing flooding schemes, Glossy's performance exhibits no noticeable dependency on node density, which facilitates its application in diverse real-world settings.
Spectrum sensing is the prerequisite of opportunistic spectrum access in cognitive sensor networks (CSNs) as its reliability determines the success of transmission. However, spectrum sensing is an energy-consuming operation that needs to be minimized for CSNs due to resource limitations. This paper considers the case where the cognitive sensors cooperatively sense a licensed channel by using the CoMAC-based cooperative spectrum sensing (CSS) scheme to determine the presence of primary users. Energy efficiency (EE), defined as the ratio of the average throughput to the average energy consumption, is a very important performance metric for CSNs. We formulate an EEmaximization problem for CSS in CSNs subject to the constraint on the detection performance. In order to address the non-convex and non-separable nature of the formulated problem, we first find the optimal expression for the detection threshold and then propose an iterative solution algorithm to obtain an efficient pair of sensing time and the length of the modulated symbol sequence. Simulations demonstrate the convergence and optimality of the proposed algorithm. It is also observed in simulations that the combination of the CoMAC-based CSS scheme and the proposed algorithm yields much higher EE than conventional CSS schemes while guaranteeing the same detection performance.
This paper presents Indriya, a large-scale, low-cost wireless sensor network testbed deployed at the National University of Singapore. Indriya uses TelosB devices and it is built on an active-USB infrastructure. The infrastructure acts as a remote programming back-channel and it also supplies electric power to sensor devices. Indriya is designed to reduce the costs of both deployment and maintenance of a large-scale testbed. Indriya has been in use by over 100 users with its maintenance incurring less than US$500 for almost 2 years of its usage.
In the second part of this paper, we provide an extensive study of all 16 channels of IEEE 802.15.4 supported by CC2420 devices constituting Indriya. The objective is to understand performance differences and correlations that may exist among different channels. The measurement results are of interest to the WSN community as they may be useful in designing WSN routing and MAC protocols that exploit channel-diversity.
We present a Software Defined Radio (SDR) based IEEE 802.15.4 transceiver testbed for GNURadio. Our testbed is Open Source and fully interoperable with off-the-shelf TelosB sensor motes and the Contiki sensor mote operating system, from the physical layer to the network stack. The testbed can be setup and configured easily via a graphical user interface and applications can interface with the SDR using TCP sockets. Furthermore, the SDR is able to log traffic in PCAP format to investigate networks with common software like Wireshark. We believe the main applications of the transceiver to be twofold. First, the communication stack has a modular, layered structure, which allows for rapid prototyping, is educational, and is easy to grasp, lowering the steep learning curve that SDRs typically have. Secondly, the integration of a network stack in GNURadio pushes interoperability from the physical to the network and application layer and thus enables the investigations of higher layer metrics with SDRs.
Wireless sensor networks (WSNs) are increasingly being applied to scenarios that simultaneously demand for high packet reliability and short delay. A promising technique to achieve this goal is concurrent transmission, i.e. multiple nodes transmit identical or different packets in parallel. Practical implementations of concurrent transmission exist, yet its performance is not well understood due to the lack of expressive models that accurately predict the success of packet reception. We experimentally investigate the two phenomena that can occur during concurrent transmission depending on the transmission timing and signal strength, i.e. constructive interference and capture effect. Equipped with the thorough understanding of these two phenomena, we propose an accurate prediction model for the reception of concurrent transmission. The extensive measurements carried out with varying number of transmitters, packet length and signal strength verify the excellent quality of our model, which provides a valuable tool for protocol design and simulation of concurrent transmission in WSNs.
It is well-known that the time taken for disseminating a large data object over a wireless sensor network is dominated by the overhead of resolving the contention for the underlying wireless channel. In this paper, we propose a new dissemination protocol called Splash, that eliminates the need for contention resolution by exploiting constructive interference and channel diversity to effectively create fast and parallel pipelines over multiple paths that cover all the nodes in a network. We call this tree pipelining. In order to ensure high reliability, Splash also incorporates several techniques, including exploiting transmission density diversity, opportunistic overhearing, channel-cycling and XOR coding. Our evaluation results on two large-scale testbeds show that Splash is more than an order of magnitude faster than state-of-the-art dissemination protocols and achieves a reduction in data dissemination time by a factor of more than 20 compared to DelugeT2.
Counting and identifying neighboring active nodes are two fundamental operations in wireless sensor networks (WSNs). In this paper, we propose two mechanisms, Power based Counting (Poc) and Power based Identification (Poid), which achieve fast and accurate counting and identification by allowing neighbors to respond simultaneously to a poller. A key observation that motivates our design is that the power of a superposed signal increases with the number of component signals under the condition of constructive interference (CI). However, due to the phase offsets and various hardware limitations (e.g., ADC saturation), the increased superposed power exhibits dynamic and diminishing returns as the number of component signals increases. This uncertainty of phase offsets and diminishing returns property of the superposed power pose serious challenges to the design of both Poc and Poid. To overcome these challenges, we design delay compensation methods to reduce the phase offset of each component signal, and propose a novel probabilistic estimation technique in cooperation with CI. We implement Poc and Poid on a testbed of 1 USRP and 50 TelosB nodes, the experimental results show that the accuracy of Poc is above 97.9%, and the accuracy of Poid is above 96.5% for most cases. In addition to their high accuracy, our methods demonstrate significant advantages over the state-of-the-art solutions in terms of substantially lower energy consumption and estimation delay.
We present a design and evaluation of Choco, a low-power, end- to-end reliable collection protocol for wireless sensor networks. We construct Choco on Glossy, which is a recently presented efficient flooding scheme exploiting constructive-interference phenomena. Although Glossy is originally developed for flooding, it can be regarded as a networking primitive which provides low-power, low- delay, any-to-any communication reachability. In Choco, all packet transmissions are based on Glossy. A sink node explicitly schedules each node operation in a centralized and fine-grained manner with Glossy, and controls retransmission and power saving mode of each node. Each sensor node transmits data packets to the sink node with Glossy according to the request from the sink node. Although Choco is very simple mechanism, we reveal that Choco has considerable merits over current data collection protocols through two different testbed evaluations. Testbed evaluations demonstrate that Choco provides 14.7 times more energy-efficient collection than the state-of-the-art, while achieving end-to-end reliability for every node.
Recent work has shown that network protocols which rely on precisely-timed concurrent transmissions can achieve reliable, energy-efficient, and conceptually simple network flooding. While these multi-transmitter schemes work well, they require all nodes in the network to forward every data packet, which has inherent inefficiencies for non-flooding traffic patterns (where not all nodes need to receive the data). In this work, we formalize the concept of the “useful” forwarder set for point-to-point transmissions in low power multi-transmitter networks, those nodes which help forward data to the destination. We present a mechanism for approximating membership in this set based on simple heuristics. Incorporating forwarder selection on our 66-node testbed reduced radio duty cycle by 30% and increased throughput by 49% relative to concurrent flooding while preserving a 99.4% end-to-end packet reception ratio under the collection traffic pattern. We envision forwarder selection as a fundamental task in an efficient multi-transmitter networking stack. This work demonstrates that adding forwarder selection can improve energy efficiency and throughput while providing similar end-to-end packet delivery rates to flooding.
In this letter, we investigate the properties of packet collisions in IEEE 802.15.4-based wireless sensor networks when packets with the same content are transmitted concurrently. While the nature of wireless transmissions allows the reception of a packet when the same packet is transmitted at different radios with (near) perfect time synchronization, we find that in practical systems, platform specific characteristics, such as the independence and error of the crystal oscillators, cause packets to collide disruptively when the two signals have similar transmission powers (i.e., differences of
We present the Flash flooding protocol for rapid network flooding in wireless sensor networks. Traditional flooding protocols can be very slow because of neighborhood contention: nodes cannot propagate the flood until neighboring nodes have finished their transmissions. The Flash flooding protocol avoids this problem by allowing concurrent transmissions among neighboring nodes. It relies on the capture effect to ensure that each node receives the flood from at least one of its neighbors, and introduces new techniques to either recover from or prevent too many concurrent transmissions. We evaluate the Flash flooding protocol on both a 48-node wireless sensor network testbed and in a trace-based simulator. Our results indicate that the Flash flooding protocol can reduce latency by as much as 80%, achieving flooding latencies near the theoretical lower bound without sacrificing coverage, reliability or power consumption.
We undertake a systematic experimental study of the ef- fects of concurrent packet transmissions in low-power wire- less networks. Our measurements, conducted with Mica2 motes equipped with CC1000 radios, confirm that guaran- teeing successful packet reception with high probability in the presence of concurrent transmissions requires that the signal-to-interference-plus-noise-ratio (SINR) exceed a crit- ical threshold. However, we find a significant variation of about 6 dB in the threshold for groups of radios operating at different transmission powers. We find that it is harder to estimate the level of interference in the presence of multiple interferers. We also find that the measured SINR threshold generally increases with the number of interferers. Our study offers a better understanding of concurrent transmissions and suggests richer interference models and useful guidelines to improve the design and analysis of higher layer protocols.
Exploiting constructive interference for scalable flooding in wireless networks
- Y Wang
- Y He
- X Mao
- Y Liu
- X Y Li