Conference
Distributed Computing in Sensor Systems
About: Distributed Computing in Sensor Systems is an academic conference. The conference publishes majorly in the area(s): Wireless sensor network & Computer science. Over the lifetime, 873 publications have been published by the conference receiving 16090 citations.
Topics: Wireless sensor network, Computer science, Key distribution in wireless sensor networks, Sensor node, Wireless network
Papers published on a yearly basis
Papers
27 Jun 2011
TL;DR: The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones and selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data.
Abstract: The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.
582 citations
30 Jun 2005
TL;DR: In this paper, the authors investigate the use of multiple mobile elements to collect and carry data mechanically from a sensor network and present a load balancing algorithm which tries to balance the number of sensor nodes each mobile element services.
Abstract: Recent research has shown that using a mobile element to collect and carry data mechanically from a sensor network has many advantages over static multihop routing. We have an implementation as well employing a single mobile element. But the network scalability and traffic may make a single mobile element insufficient. In this paper we investigate the use of multiple mobile elements. In particular, we present load balancing algorithm which tries to balance the number of sensor nodes each mobile element services. We show by simulation the benefits of load balancing.
434 citations
30 Jun 2005
TL;DR: A distributed weight-based energy-efficient hierarchical clustering protocol (DWEHC), where each node first locates its neighbors, then calculates its weight which is based on its residual energy and distance to its neighbors.
Abstract: Since nodes in a sensor network have limited energy, prolonging the network lifetime and improving scalability become important. In this paper, we propose a distributed weight-based energy-efficient hierarchical clustering protocol (DWEHC). Each node first locates its neighbors (in its enclosure region), then calculates its weight which is based on its residual energy and distance to its neighbors. The largest weight node in a neighborhood may become a clusterhead. Neighboring nodes will then join the clusterhead hierarchy. The clustering process terminates in O(1) iterations, and does not depend on network topology or size. Simulations show that DWEHC clusters have good performance characteristics.
320 citations
5 Jun 2017
TL;DR: This paper is the first to present a thorough analysis of the impact of LoRa transmission parameter selection on communication performance, and develops a link probing regime which enables us to quickly determine transmission settings that satisfy performance requirements.
Abstract: Low-Power Wide-Area Network (LPWAN) technologies such as Long Range (LoRa) are emerging that enable power efficient wireless communication over very long distances. LPWAN devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. However, to ensure efficient and reliable communication LPWAN devices often provide a large number of transmission parameters. For example, a LoRa device can be configured to use different spreading factors, bandwidth settings, coding rates and transmission powers, resulting in over 6720 possible settings. It is a challenge to determine the setting that minimises transmission energy cost while meeting the required communication performance. This paper is the first to present a thorough analysis of the impact of LoRa transmission parameter selection on communication performance. We study in detail the impact of parameter settings on energy consumption and communication reliability. Using this study we develop a link probing regime which enables us to quickly determine transmission settings that satisfy performance requirements. The presented work is a first step towards an automated mechanism for LoRa transmission parameter selection that a deployed LoRa network requires, but is not yet specified within the Long Range Wide Area Network (LoRaWAN) framework.
304 citations
26 May 2016
TL;DR: For the first time WiFi signals can also be used to uniquely identify people and a system called WiFi-ID is proposed that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow for uniquely identify that person.
Abstract: Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.
300 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 69 |
| 2020 | 68 |
| 2019 | 105 |
| 2018 | 24 |
| 2017 | 32 |
| 2016 | 39 |