The Internet of Things vision: Key features, applications and open issues

Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Energy harvesting in wireless sensor networks: A comprehensive review

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree.

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Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Computer communications

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Afsar%20-%20Clustering%20in%20Sensor%20Networks%20-%202014.pdf

Clustering in sensor networks

RFID, capable of remote automatic identification, is taking the place of barcodes to become electronic tags of the new generation. However, the information transmitted in the air could easily be intercepted and eavesdropped due to its radio transmission nature. On top of this, its prevalence has brought the stress on its security and privacy issues. EPC Class 1 Generation 2 (Gen 2) has served as the most popular standard for passive tags. Passive tags possess limited computation ability and capacity that just makes designing of the security protocol even more challenging. Researchers have proposed quite a few security protocols for RFID, but most of them are just too complicated to be implemented on Gen 2. Chien and Chen (2007) proposed a mutual authentication protocol conforming to this standard. However, it is found vulnerable to DoS attacks. Due to the bad properties of the CRC function used in the protocol, the claimed security objectives are also not met. Moreover, the database must use brute search for each tag's authentication. This paper will give demonstrations on what have caused these weaknesses, and more of that, an improved protocol is also proposed which are free from worries of the problems mentioned above. The improved protocol could thus be applied in high security demanding environments.

Securing RFID systems conforming to EPC Class 1 Generation 2 standard

In wireless sensor networks, nodes in the area of interest must report sensing readings to the sink, and this report always satisfies the report frequency required by the sink. This paper proposes a link-aware clustering mechanism, called LCM, to determine an energy-efficient and reliable routing path. The LCM primarily considers node status and link condition, and uses a novel clustering metric called the predicted transmission count (PTX), to evaluate the qualification of nodes for clusterheads and gateways to construct clusters. Each clusterhead or gateway candidate depends on the PTX to derive its priority, and the candidate with the highest priority becomes the clusterhead or gateway. Simulation results validate that the proposed LCM significantly outperforms the clustering mechanisms using random selection and by considering only link quality and residual energy in the packet delivery ratio, energy consumption, and delivery latency.

Sheng-Shih Wang

Papers

Securing RFID systems conforming to EPC Class 1 Generation 2 standard

LCM: A Link-Aware Clustering Mechanism for Energy-Efficient Routing in Wireless Sensor Networks

PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks

CollECT: Collaborative event detection and tracking in wireless heterogeneous sensor networks

Distributed direction-based localization in wireless sensor networks