Network management framework and lifetime evaluation method for wireless sensor networks

Kim, Jae-Woo; Jeon, HahnEarl; Lee, Jaiyong

doi:10.3233/ica-2012-0397

Cited by 18 publications

(9 citation statements)

References 26 publications

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“…However, in the literature few works address energy consumption models for communication devices. Kim et al [24] focus on the management framework of wireless sensor networks, analyze the energy efficient network management by reducing management traffic, and propose a lifetime estimation algorithm. Hereinto, the energy consumption is estimated from network management information, without modeling the energy consumption of the nodes.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of smart grid communication network by simulation

Luo

Sansavini

Zio

2015

ICA

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In this paper, we study the reliability of a communication network at the feeder level of the distribution system of a smart grid. WiFi Mesh is indicated as the superior technology for smart grid applications at feeder level based upon availability and cost considerations. In this context, we provide a model for evaluating the transmission delay and energy consumption in the WiFi network, and relate these quantities to the reliability of message transmission of the network. The Energy Consumption Aware Shortest Path (ECASP) algorithm is developed to search the shortest delay routing for each message and keep updating the available energy capacity of each communication device. Simulation procedures are presented to get estimates of the delay of different types of messages and compute the communication network lifetime and message transmission reliability. The proposed approach is exemplified with reference to a realistic distribution system of a smart grid, with three types of messages exchanged at the feeder level.

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Section: Introductionmentioning

confidence: 99%

Reliability analysis of smart grid communication network by simulation

Luo

Sansavini

Zio

2015

ICA

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“…So, our lifetime can be defined as the time until the region of interest is covered by sensor nodes. [9] Figure 11. Traffic hotspots problem -11% In this process, we simulated the process of Traffic hotspot problem in figure 11.…”

Section: Simulation Resultsmentioning

confidence: 97%

Lifetime improvement method using mobile sink for IoT service

Kim

Lee

2013

Proceedings of the 10th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, &Amp; Ubiquitous Networks

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In this paper, we describe a lifetime improvement method using mobile sink node for Internet of Things (IoT) device and discuss the major technologies involved in IoT. Additionally, we discuss the potential scope of IoT and the major technologies involved in IoT infrastructure. Conventional network was based on static sink which is unsuitable for resource constrained Wireless Sensor Networks (WSNs). WSNs can be large scale networks and it is impossible to perform as normal without connectivity. In IoT, physical objects home appliances, temperature sensor, smart phone, table PC etc. will be context aware to sense the environment around them and interact with each other. The devices in IoT are powered by a battery with limited power, which is dissipated during sensing, processing and data transmission/reception. For IoT, devices are required to be made sustainability by embedding connectivity and intelligent into them using technologies such as WSNs. Therefore connectivity and energy efficiency are the key issues in WSNs. We propose a simple method using mobile sink based on Travelling Salesman Problem (TSP). The proposed method uses less memory and less overhead cost than other schemes including relay node, and is therefore appropriate for implementation in WSNs. We will prove that the method can guarantee network connectivity. The performance of our method is demonstrated by simulation results. Not only simulation result shows that the proposed method can extend the network lifetime and connectivity, but also reduces unnecessary relay node's energy consumption by using mobile sink.

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“…As sensor nodes with networking capabilities become commercially available in recent years, wireless sensor networks (WSNs) have been increasingly used in industrial and civilian applications as well as in military applications; e.g., industrial process monitoring and control, environment and habitat monitoring, traffic control, and battlefield surveillance [13]. When sensor networks are deployed in unattended or adversarial environments, security becomes a major concern [2].…”

Section: Introductionmentioning

confidence: 99%

M-cluster and X-ray: Two methods for multi-jammer localization in wireless sensor networks

Cheng

Zhu

et al. 2014

ICA

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Jamming is one of the most severe attacks in wireless sensor networks (WSNs). While existing countermeasures mainly focus on designing new communication mechanisms to survive under jamming, a proactive solution is to first localize the jammer(s) and then take necessary actions. Unlike the existing work that focuses on localizing a single jammer in WSNs, this work solves a multi-jammer localization problem, where multiple jammers launch collaborative attacks. We develop two multijammer localization algorithms: a multi-cluster localization (M-cluster) algorithm and an X-rayed jammed-area localization (Xray) algorithm. Our extensive simulation results demonstrate that with one run of the algorithms, both M-cluster and X-ray are efficient in localizing multiple jammers in a wireless sensor network with small errors.

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Network management framework and lifetime evaluation method for wireless sensor networks

Cited by 18 publications

References 26 publications

Reliability analysis of smart grid communication network by simulation

Reliability analysis of smart grid communication network by simulation

Lifetime improvement method using mobile sink for IoT service

M-cluster and X-ray: Two methods for multi-jammer localization in wireless sensor networks

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