Improving the performance of trickle-based data dissemination in low-power networks

Trickle is a polite gossip algorithm for managing communication traffic. It is of particular interest in low-power wireless networks for reducing the amount of control traffic, as in routing protocols (RPL), or reducing network congestion, as in multicast protocols (MPL). Trickle is used at the network or application level, and relies on up-to-date information on the activity of neighbors. This makes it vulnerable to interference from the media access control layer, which we explore in this paper. We present several scenarios how the MAC layer in low-power radios violates Trickle timing. As a case study, we analyze the impact of CSMA/CA with ContikiMAC on Trickle's performance. Additionally, we propose a solution called Cleansing that resolves these issues

Energy-aware reprogramming of sensor networks using incremental update and compression

Reprogramming is an important issue in wireless sensor networks. It enables users to extend or correct functionality of a sensor network after deployment at a low cost. In this paper, we investigate the problem of improving energy efficiency and delay of reprogramming by using data compression and incremental updates. We analyze different algorithms for both approaches, as well as their combination, when applied to resource-constrained devices. Our results show that the classic Lempel-Ziv-77 compression algorithm with Bsdiff for delta encoding has the best overall performance compared to other compression algorithms; on average reducing energy usage by 74% and enabling 71% faster updates

Patching a patch : software updates using horizontal patching

This paper presents a method for optimizing software updates of consumer electronic devices running multiple applications with a common software component, called horizontal patching. Instead of using separate deltas for patching different applications, the method generates one delta from the other. Due to the large similarities between the deltas, this horizontal delta is small in size. Experimental results on two test sets, consisting of software updates for sensor networks and smart phones, show that significant improvements can be achieved. Between 27% and 84% data can be saved from transmission, depending on the type of applications and shared components

Efficient reprogramming of wireless sensor networks using incremental updates

Software reprogramming enables users to extend or correct functionality of a sensor network after deployment, preferably at a low cost. This paper investigates the improvement of energy efficiency and delay of reprogramming, at low resource cost. As enabling technologies data compression and incremental updates are used. Algorithms for both approaches are analyzed, as well as their combination, applied to resource-constrained devices. All algorithms are ported to the Contiki operating system, and profiled for different types of reprogramming. The presented results show that there is a clear trade-off between performance and resource requirements. Furthermore, the best reprogramming approach depends on the type of update. Experimentally, VCDIFF, or the combination of Lempel-Ziv-77/FastLZ for compression with BSDIFF for delta encoding, have been identified as the best possible options

Adaptive broadcast suppression for Trickle-based protocols

Low-power wireless networks play an important role in the Internet of Things. Typically, these networks consist of a very large number of lossy and low-capacity devices, challenging the current state of the art in protocol design. In this context the Trickle algorithm plays an important role, serving as the basic mechanism for message dissemination in notable protocols such as RPL and MPL. While Trickle's broadcast suppression mechanism has been proven to be efficient, recent work has shown that it is intrinsically unfair in terms of load distribution and that its performance relies strongly on network topology. This can lead to increased end-to-end delays (MPL), or creation of sub-optimal routes (RPL). Furthermore, as highlighted in this work, there is no clear consensus within the research community about what the proper parameter settings of the suppression mechanism should be. We propose an extension to the Trickle algorithm, called adaptive-k, which allows nodes to individually adapt their suppression mechanism to local node density. Supported by analysis and a case study with RPL, we show that this extension allows for an easier configuration of Trickle, making it more robust to network topology

Patching a patch : software updates using horizontal patching

This paper presents a method for optimizing incremental updates of consumer electronic devices running multiple applications, called horizontal patching. Instead of using separate deltas for patching different applications, the method generates one delta from the other. Due to the large similarities between the deltas, this horizontal delta is small in size. In all test cases horizontal patching produced smaller deltas, with compression ratios between 8.02% and 43.38%

Proxy support for service discovery using mDNS/DNS-SD in low power networks

We present a solution for service discovery of resource constrained devices based on mDNS/DNS-SD. We extend the mDNS/DNS-SD service discovery protocol with support for proxy servers. Proxy servers temporarily store information about services offered on resource constrained devices and respond on their behalf while they are not available. We analyze two protocols for the delegation mechanism between a service provider and a proxy server: an active proxy protocol, as used in the mDNS/DNS-SD implementation by Apple, and a new, passive proxy protocol. We implement and simulate both approaches. Based on the delay and energy usage, we show that the second approach converges faster, thus saving more energy by allowing the resource constrained device to be turned off earlier

Energy-aware reprogramming of sensor networks using incremental update and compression

Reprogramming is an important issue in wireless sensor networks. It enables users to extend or correct functionality of a sensor network after deployment at a low cost. In this paper, we investigate the problem of improving energy efficiency and delay of reprogramming by using data compression and incremental updates. We analyze different algorithms for both approaches, as well as their combination, when applied to resource-constrained devices. Our results show that the classic Lempel-Ziv-77 compression algorithm with Bsdiff for delta encoding has the best overall performance compared to other compression algorithms; on average reducing energy usage by 74% and enabling 71% faster updates