Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks, and Computation

Freris, Nikolaos M.; Kowshik, Hemant; Kumar, P. R.

doi:10.1109/jproc.2010.2065790

Cited by 107 publications

(97 citation statements)

References 74 publications

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“…blockage, while inertial-based techniques become inaccurate over time due to velocity drift. Cooperative techniques are attracting increasing interest for localization [6]- [8], [26]- [28], driven by their success in wireless communications [29]- [32]. Such techniques have been shown to improve localization performance due to sharing of information among spatial neighbors [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Network Navigation: Theory and Interpretation

Shen

Mazuelas

Win

2012

IEEE J. Select. Areas Commun.

150

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Abstract-Real-time and reliable location information of mobile nodes is a key enabler for many emerging wireless network applications. Such information can be obtained via network navigation, a new paradigm in which nodes exploit both spatial and temporal cooperation to infer their positions. In this paper, we establish a theoretical foundation for network navigation and determine the fundamental limits of navigation accuracy using equivalent Fisher information analysis. We then introduce the notion of carry-over information and provide a geometrical interpretation for the evolution of navigation information. Our framework unifies the navigation information obtained from spatial and temporal cooperation, leading to a deep understanding of information evolution and cooperation benefits in navigation networks.Index Terms-Cooperative network, localization, navigation, Cramér-Rao bound (CRB), equivalent Fisher information (EFI).

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Section: Introductionmentioning

confidence: 99%

Network Navigation: Theory and Interpretation

Shen

Mazuelas

Win

2012

IEEE J. Select. Areas Commun.

150

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“…The nodes form an ad hoc multihop network, communicating readings to base stations. The connectivity of these self-organizing networks is critical for reliable sensing and inference capabilities [12].…”

Section: Applications Of Secrecy Transfermentioning

confidence: 99%

Secrecy Transfer for Sensor Networks: From Random Graphs to Secure Random Geometric Graphs

Liu¹,

Ma²,

Zeng³

2013

International Journal on Smart Sensing and Intelligent Systems

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Suppose n nodes with n 0 acquaintances per node are randomly deployed in a two-dimensional Euclidean space with the geographic restriction that each pair of nodes can exchange information between them directly only if the distance between them is at most r, the acquaintanceship between nodes form a random graph, while the physical communication links constitute a random geometric graph. To get a fully connected and secure graph, we introduce a secrecy transfer algorithm which combines the random graph and the random geometric graph via an introduction process to produce an acquaintanceship graph G n,n0 . We find that the maximum component of graph G n,n0 transitions rapidly from small components to a giant component when n 0 is larger than a threshold, the threshold is derived, and applications for sensor networks are presented.

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“…Thus, when the PLR crosses a high or low threshold, a comparison according to (8) is performed, and a weight matrix, , is formed similar to the one in (1). Edges (1,2) or (1,3) …”

Section: Designing a Wcm For Transmit Power Data Rate Andmentioning

confidence: 99%

A Cross-Layer Framework for Network Management in Wireless Sensor Networks Using Weighted Cognitive Maps

Elmougy

Ibnkahla

2013

International Journal of Distributed Sensor Networks

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Achieving the end-to-end goals and objectives of Wireless Sensor Networks (WSN) is a highly challenging task. Such objectives include maximizing network lifetime, guaranteeing connectivity and coverage, and maximizing throughput. In addition, some of these goals are in conflict such as network lifetime and throughput. Cross-layer design can be efficient in proposing network management techniques that can consider different network objectives and conflicting constraints. This can be highly valuable in challenging applications where multiple Quality of Service (QoS) requirements may be demanded. In this paper, a novel crosslayer framework for network management is proposed that particularly targets WSN with challenging applications. The proposed framework is designed using the tool known as Weighted Cognitive Map (WCM). The inference properties of WCMs allow the system to consider multiple objectives and constraints while maintaining low complexity. Methods for achieving different objectives using WCMs are illustrated, as well as how system processes can operate coherently to achieve common end-to-end goals. Using extensive computer simulations, the proposed system is evaluated. The results show that it achieves good performance results in metrics of network lifetime, throughput, and Packet Loss Ratio (PLR).

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Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks, and Computation

Cited by 107 publications

References 74 publications

Network Navigation: Theory and Interpretation

Network Navigation: Theory and Interpretation

Secrecy Transfer for Sensor Networks: From Random Graphs to Secure Random Geometric Graphs

A Cross-Layer Framework for Network Management in Wireless Sensor Networks Using Weighted Cognitive Maps

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