Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-ModalWireless Sensor Networks

Hammoudeh, Mohammad; Newman, Robert; Dennett, Christopher; Mount, Sarah; Aldabbas, Omar

doi:10.3390/s150922970

Cited by 46 publications

(21 citation statements)

References 22 publications

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“…Several hierarchical architectures have been considered for various applications of mobile WSNs [7,[11][12][13][14][15]. In some approaches, cluster heads are used as MDCs besides their sensing duties.…”

Section: Related Workmentioning

confidence: 99%

Dynamic clustering and management of mobile wireless sensor networks

et al. 2017

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In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDCrelay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to 53%.

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Section: Related Workmentioning

confidence: 99%

Dynamic clustering and management of mobile wireless sensor networks

et al. 2017

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“…There has been continuous research into Service-Oriented Architecture (SOA) for highly decentralized systems, such as Internet of Things (IoT) over the last decade [10]. The authors of [12] proposed an SOA-based application development model, a standardized interface to retrieve network data, and a group of configurable service components to provision the implementation of applications and to manage the network behavior at runtime.…”

Section: Related Workmentioning

confidence: 99%

A Light Weight Service Oriented Architecture for the Internet of Things

Aldabbas¹

2016

ijacsa

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Abstract-Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and implementation of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval, considering objects as highly decentralized, composite and costsufficient services. Such services are constructed from objects located within close geographical proximity to retrieve spatiotemporal events from the gathered sensor data. To achieve this, we advocate coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to accomplish the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70% while maintaining energy consumption 20% lower than its best rivals in the literature.

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“…Wireless Multimedia Sensor Network (WMSN) are multi-hop networks collective a huge amount of sensor. It may be camera sensors or scalar sensors which scattered with the enormous environment to gather multimedia contents by means of different concern like audio, image, and video [10,15]. Each sensor has the ability to connect with several other sensors to reach a Base Station (BS) that is the whole network escape in the digital world in WMSN [1,3].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of DSR and AODV Routing Protocols for Soft Delay Deadlines in Wireless Multimedia Sensor Network

Sarwat¹,

Tahir²,

Awan³

et al. 2018

Pakistan Journal of Engineering, Technology & Science

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Wireless Multimedia Sensor Network (WMSN) is a collection of the vast amount of different types of sensors like camera sensor, video and scalar sensors which are involved in retrieving multimedia data from the large environment. The real-time sending of video and audio content to the destination before a strict playout deadline has been necessary for multimedia environment. Otherwise, it will be dropped at the destination. In WMSN sending real time multimedia data with soft play deadlines is a challenging task to solve this challenge, routing protocols play an important role in WMSN. Routing demands of multimedia content of WMSNs need to be perfect routing protocols to optimize path selection and guarantee communication. This paper presents a performance comparison between two reactive routing protocols; namely AODV and DSR, with soft delay deadlines and efficient utilization of resources in WMSN. The objective is to assess the real-time behavior of these two protocols upon sending multimedia content. Here, we evaluate the performance with respect to the use of these matrices like latency, Average jitter, Average delay and throughput and factors includes are CBR and multimedia traffic with varying packet size and bandwidth. DSR perform better as compared to AODV routing protocol since it discovers the routes more efficiently. AODV is better in term of Jitter than DSR. NS-2 simulator tool used for the purpose of this comparison.

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Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-ModalWireless Sensor Networks

Cited by 46 publications

References 22 publications

Dynamic clustering and management of mobile wireless sensor networks

Dynamic clustering and management of mobile wireless sensor networks

A Light Weight Service Oriented Architecture for the Internet of Things

Performance Comparison of DSR and AODV Routing Protocols for Soft Delay Deadlines in Wireless Multimedia Sensor Network

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