Wireless Sensor Network: At a Glance

Dwivedi, Ananya; Vyas, O. P.

doi:10.5772/19005

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…We do not directly consider hardware-specific aspects, but there are three key hardware features: re-programmable program memory (e.g., FLASH), a temporary store (e.g., EEPROM), and a secure location for a fallback image (e.g., Boot-ROM). An overview of WSN platforms is given in [54][55][56][57].…”

Section: The Execution Environmentmentioning

confidence: 99%

Software Updating in Wireless Sensor Networks: A Survey and Lacunae

Brown

Sreenan

2013

JSAN

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Wireless Sensor Networks are moving out of the laboratory and into the field. For a number of reasons there is often a need to update sensor node software, or node configuration, after deployment. The need for over-the-air updates is driven both by the scale of deployments, and by the remoteness and inaccessibility of sensor nodes. This need has been recognized since the early days of sensor networks, and research results from the related areas of mobile networking and distributed systems have been applied to this area. In order to avoid any manual intervention, the update process needs to be autonomous. This paper presents a comprehensive survey of software updating in Wireless Sensor Networks, and analyses the features required to make these updates autonomous. A new taxonomy of software update features and a new model for fault detection and recovery are presented. The paper concludes by identifying the lacunae relating to autonomous software updates, providing direction for future research

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Section: The Execution Environmentmentioning

confidence: 99%

Software Updating in Wireless Sensor Networks: A Survey and Lacunae

Brown

Sreenan

2013

JSAN

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“…In monitoring phase, the user interest is mainly focuses on the values read by network sensors. In controlling phase, the application can also be used to control WSNs by sending notification to the sensor network [9].…”

Section: Iintroductionmentioning

confidence: 99%

A SURVEY: Duty cycle based routing and scheduling in wireless sensor networks

Nithya¹,

Mahendran²

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Wireless sensor networks consist of spatially distributed sensor nodes, which can sense the environment by collecting, processing and transmitting the data to sink node. A critical issue in wireless sensor networks since most sensors is equipped with non-rechargeable batteries. The lifetime of a sensor network can be extended by jointly applying different techniques of scheduling and routing schemes also brings great challenges to the design of efficient distributed routing protocols for multi-hop wireless sensor networks. In scheduling algorithms, the nodes are arranged to sleep when they are not in use. The main purpose of scheduling algorithms is to minimize resource starvation and to ensure fairness amongst the parties utilizing the resources. The routing can be applied to wireless sensor networks for reducing energy consumption caused by retransmissions and dynamically detouring critical sensor nodes with less energy. To achieve network lifetime maximization of wireless sensor network through joint routing schemes and scheduling algorithms. This paper surveys the latest progresses in scheduling and routing schemes.

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“…WSN radios support the tradeoff between lower data rates and longer range versus faster data rates and lower range. Of the devices that support wireless communications in [30], the majority offer data rates from 10 kbps up to 250 kbps. Therefore the developed framework should operate with available data rates within this range.…”

Section: Communications Capabilitymentioning

confidence: 99%

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

Valencia¹

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This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices.The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic -specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their "native" logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device.IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance.Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and iii iv Abstract slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions.Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an "acceptable" average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were com...

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Wireless Sensor Network: At a Glance

Cited by 5 publications

References 32 publications

Software Updating in Wireless Sensor Networks: A Survey and Lacunae

Software Updating in Wireless Sensor Networks: A Survey and Lacunae

A SURVEY: Duty cycle based routing and scheduling in wireless sensor networks

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

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