Detection and Localization Sensor Assignment with Exact and Fuzzy Locations

Rowaihy, Hosam; Johnson, Matthew; Pizzocaro, Diego; Bar-Noy, Amotz; Kaplan, Lance; Porta, Thomas F. La; Preece, Alun

doi:10.1007/978-3-642-02085-8_3

Cited by 13 publications

(24 citation statements)

References 24 publications

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“…Our work extends on [8] which studied a closely related problem. In this paper, the authors considered both event detection and target localization problems and proposed solutions that use exact and fuzzy location.…”

Section: Related Workmentioning

confidence: 93%

“…Our problem definition is similar to the one proposed in [8], which we explain next. The goal is to assign sensors to multiple ongoing tasks such that the sum of achieved profits from all tasks is maximized.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The utilities in this case are monotonic, that is, each sensor potentially raises the detection probability further but in nonlinear fashion. This problem has been shown to be NP-hard [8].…”

Section: Problem Definitionmentioning

confidence: 99%

“…(1) initialize each e ij = e ij , the detection probability of S i for T i (2) initialize each task cumulative detection probability u j ← 0 (3) initialize number of assigned sensors to T j , n j ← 0 For leader of taskT j (4) advertise presence of T j to each neighboring sensor S i (5) for round = 0 to R do (6) if n j ≤ N then (7) among responding sensors G, choose i ← arg max i {e ij : S i ∈ G} (8) update u j ← u j + e ij (9) send accept messages and advertise new u j (10) else (11) done (12) …”

Section: Generic Assignment Algorithmmentioning

confidence: 99%

“…As in [8], we use the following to calculate the detection probability when sensor S i is assigned to task T j :…”

Section: Generic Assignment Algorithmmentioning

confidence: 99%

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Location Privacy and Energy Preservation in Sensor Allocation Systems

Rowaihy

2012

International Journal of Distributed Sensor Networks

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We propose sensor-to-task assignments algorithms that take into account location privacy issues. Our solutions enable the network to choose the "best" assignment of the available sensors to the tasks to maximize the utility of the network while preserving sensors' location privacy. We focus on preserving the sensors' locations during the assignment process in contrast to previous works, which considered location privacy while sensors are in operation. We also propose an energy-based algorithm that uses the remaining energy of a sensor as a factor to scale the distance between a sensor and a task such that sensors that have more energy appear to be closer than ones with less energy left. This does not only provide location privacy but will also consume the sensing resources more evenly, which leads to extending the lifetime of the network. From our simulations, we found that our algorithms can successfully hide sensors' locations while providing performance close to the algorithms, which use the exact locations. In addition, our energybased algorithm, while achieving results close to the exact location algorithm, was found to increase the network lifetime by as much as 40%.

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Section: Related Workmentioning

confidence: 93%

Section: Problem Definitionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Generic Assignment Algorithmmentioning

confidence: 99%

“…As in [8], we use the following to calculate the detection probability when sensor S i is assigned to task T j :…”

Section: Generic Assignment Algorithmmentioning

confidence: 99%

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Location Privacy and Energy Preservation in Sensor Allocation Systems

Rowaihy

2012

International Journal of Distributed Sensor Networks

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Wireless Sensor Networks with Energy Harvesting

Basagni¹,

Naderi²,

Petrioli³

et al. 2013

Mobile Ad Hoc Networking

140

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This chapter covers the fundamental aspects of energy harvesting-based wireless sensor networks (EHWSNs), ranging from the architecture of an EHWSN node and of its energy subsystem, to protocols for task allocation, MAC, and routing, passing through models for predicting energy availability. With the advancement of energy harvesting techniques, along with the development of small factor harvesters for many different energy sources, EHWSNs are poised to become the technology of choice for the host of applications that require the network to function for years or even decades. Through the definition of new hardware and communication protocols specifically tailored to the fundamentally different models of energy availability, new applications can also be conceived that rely on "perennial" functionalities from networks that are truly self-sustaining and with low environmental impact. 703 704 WIRELESS SENSOR NETWORKS WITH ENERGY HARVESTING hardware, protocol stack design, localization and tracking techniques, and energy management [1].Research on WSNs has been driven (and somewhat limited) by a common focus: energy efficiency. Nodes of a WSN are typically powered by batteries. Once their energy is depleted, the node is "dead." Only in very particular applications can batteries be replaced or recharged. However, even when this is possible, the replacement/ recharging operation is slow and expensive and decreases network performance. Different techniques have therefore been proposed to slow down the depletion of battery energy, which include power control and the use of duty cycle-based operation. The latter technique exploits the low power modes of wireless transceivers, whose components can be switched off for energy saving. When the node is in a low power (or "sleep") mode its consumption is significantly lower than when the transceiver is on [2,3]. However, when asleep the node cannot transmit or receive packets. The duty cycle expresses the ratio between the time when the node is on and the sum of the times when the node is on and asleep. Adopting protocols that operate at very low duty cycles is the leading type of solution for enabling long-lasting WSNs [4]. However, this approach suffers from two main drawbacks: (1) There is an inherent tradeoff between energy efficiency (i.e., low duty cycling) and data latency, and (2) battery operated WSNs fail to provide the needed answer to the requirements of many emerging applications that demand network lifetimes of decades or more. Battery leakage and aging deplete batteries within a few years, even if they are seldom used [5,6]. For these reasons, recent research on long-lasting WSNs is taking a different approach, proposing energy harvesters combined with the use of rechargeable batteries and super-capacitors (for energy storage) as the key enabler to "perpetual" WSN operations.Energy-Harvesting-based WSNs (EHWSNs) are the result of endowing WSN nodes with the capability of extracting energy from the surrounding environment. Energy harvesting can exploit different sou...

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Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach

Preece

2019

Mission-Oriented Sensor Networks and Systems: Art and Science

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Detection and Localization Sensor Assignment with Exact and Fuzzy Locations

Cited by 13 publications

References 24 publications

Location Privacy and Energy Preservation in Sensor Allocation Systems

Location Privacy and Energy Preservation in Sensor Allocation Systems

Wireless Sensor Networks with Energy Harvesting

Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach

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