Study on Distance of Interference Sources on Wireless Sensor Network

Vanheel, Frank; Verhaevert, Jo; Moerman, Ingrid

doi:10.1109/eumc.2008.4751416

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Table 2 shows the different modulations of different standard versions. Vanheel et al [45] report similar observations, claiming that OFDM has a wider spectrum than DSSS. This effect will become clearer in the following experiments (see Table 5).…”

Section: Heavy Channel Utilizationsupporting

confidence: 65%

A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

Zacharias

Newe

O'Keeffe

et al. 2014

International Journal of Distributed Sensor Networks

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IEEE 802.15.4 is the technology behind wireless sensor networks (WSNs) and ZigBee. Most of the IEEE 802.15.4 radios operate in the crowded 2.4 GHz frequency band, which is used by many technologies. Since IEEE 802.15.4 is a low power technology, the avoidance of interference is vital to conserve energy and to extend the lifetime of devices. A lightweight classification algorithm is presented to detect the common external sources of interference in the 2.4 GHz frequency band, namely, IEEE 802.11-based wireless local area networks (WLANs), Bluetooth, and microwave ovens. This lightweight algorithm uses the energy detection (ED) feature (the feature behind received signal strength indication (RSSI)) of an IEEE 802.15.4-compliant radio. Therefore, it classifies the interferers without demodulation of their signals. As it relies on time patterns instead of spectral features, the algorithm has no need to change the channel. Thus, it allows the radio both to stay connected to the channel and to receive while scanning. Furthermore, it has a maximum runtime of merely one second. The algorithm is extensively tested in a radio frequency anechoic chamber and in real world scenarios. These results are presented here.

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Section: Heavy Channel Utilizationsupporting

confidence: 65%

A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

Zacharias

Newe

O'Keeffe

et al. 2014

International Journal of Distributed Sensor Networks

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“…While the Fig. 1 Cluster-based dual-radio WSN architecture coexistence issues of IEEE 802.15.4 and IEEE 802.11 have been studied in existing literature [1][2][3][4][5][6][7][8][9][10][11], main concern of previous research is on how independent IEEE 802.11 network and IEEE 802.15.4 network interfere with each other when the overlapping channels used by those two networks.…”

Section: Cluster-based Dual-radio Wsn Architecturementioning

confidence: 99%

Interference mitigation in wireless sensor networks using dual heterogeneous radios

2011

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Scalability is one of the most important features that future wireless sensor networks (WSNs) should provide, and clustering is widely considered as a viable approach for high scalability. In the cluster-based architecture, the cluster heads play a key role in relaying messages between the sensor nodes and the sink. While the cluster heads are involved in both intra-cluster and intercluster communication, the latter typically requires transmission over much longer distance than the former. In this paper, we consider a scenario in which each cluster head is equipped with dual radios: IEEE 802.15.4 and IEEE 802.11 for intra-cluster and inter-cluster communication, respectively. IEEE 802.11 links between the cluster heads and the sink provide a high capacity backbone for large-scale WSNs. IEEE 802.15.4 and IEEE 802.11 share a lot of similarities including CSMA/CA MAC. Their operating spectrum also overlaps at the 2.4 GHz ISM band, and this may cause interference. We first experimentally measure how severe the interference can be, when two radios are concurrently used in a WSN. We, then, propose an interference mitigation solution which relies on adaptive aggregation of packets and adaptive transmission scheduling. Through prototyping and experimental evaluation, we show that the proposed scheme significantly reduces the interferences between the two types of radios.

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“…Every packet consists of 100 bytes, and the inter packet delay is 25 ms. Transmission is at channel 26 to avoid WiFi interference [41]. Upon swapping sending nodes, the test bed remains idle for 8 ms.…”

Section: Multipath Fadingmentioning

confidence: 99%

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Vanheel

Verhaevert

Laermans

et al. 2011

J Wireless Com Network

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Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.

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Study on Distance of Interference Sources on Wireless Sensor Network

Cited by 8 publications

References 4 publications

A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

Interference mitigation in wireless sensor networks using dual heterogeneous radios

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

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