Efficient Aggregation of Multiple Classes of Information in Wireless Sensor Networks

Qiu, Xiaoling; Li, Haiping; Li, Deshi; Yick, Jennifer; Ghosal, Dipak; Mukherjee, Biswanath

doi:10.3390/s91008083

Cited by 26 publications

(7 citation statements)

References 17 publications

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“…Several solutions have been proposed in the literature addressing these issues [ 24 ]. At the same time, considerable effort has been devoted to the design of energy efficient message delivery and data retrieval methods [ 25 ]. Theoretically, point-to-point systems are the most reliable because there is only one point of failure in the topology (the host).…”

Section: Discussionmentioning

confidence: 99%

Wireless Sensor Networks for Oceanographic Monitoring: A Systematic Review

Albaladejo

Sánchez²,

Iborra³

et al. 2010

Sensors

146

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Monitoring of the marine environment has come to be a field of scientific interest in the last ten years. The instruments used in this work have ranged from small-scale sensor networks to complex observation systems. Among small-scale networks, Wireless Sensor Networks (WSNs) are a highly attractive solution in that they are easy to deploy, operate and dismantle and are relatively inexpensive. The aim of this paper is to identify, appraise, select and synthesize all high quality research evidence relevant to the use of WSNs in oceanographic monitoring. The literature is systematically reviewed to offer an overview of the present state of this field of study and identify the principal resources that have been used to implement networks of this kind. Finally, this article details the challenges and difficulties that have to be overcome if these networks are to be successfully deployed.

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Section: Discussionmentioning

confidence: 99%

Wireless Sensor Networks for Oceanographic Monitoring: A Systematic Review

Albaladejo

Sánchez²,

Iborra³

et al. 2010

Sensors

146

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show abstract

“…2) Value-based pricing: The value-based pricing can be also employed for the packet dropping strategy at the congested node as proposed in [155]. However, in this approach, the buyer sets the price instead of the seller.…”

Section: Congestion Managementmentioning

confidence: 99%

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Luong

Hoang

Wang

et al. 2016

IEEE Commun. Surv. Tutorials

303

124

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Abstract-This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.

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“…Well-known CBRP methods include LCA (Linked Clustered Algorithm) [17], LID (Lowest-ID) [9], LCC (Least Cluster Change) [18], MCC (Maximum Connectivity) [19], and RCC (Random Competition Clustering) [20]. These existing algorithms have clustering criteria for selecting cluster heads and are based on the minimum cluster overlap method in the formation of clusters [21,22]. These algorithms, however, cannot guarantee stability due to the ambiguity in the selection of cluster heads.…”

Section: Related Workmentioning

confidence: 99%

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

Lee

Jeong

2011

Sensors

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Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

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Efficient Aggregation of Multiple Classes of Information in Wireless Sensor Networks

Cited by 26 publications

References 17 publications

Wireless Sensor Networks for Oceanographic Monitoring: A Systematic Review

Wireless Sensor Networks for Oceanographic Monitoring: A Systematic Review

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

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