A knapsack approach to sensor-mission assignment with uncertain demands

Pizzocaro, Diego; Johnson, Matthew; Rowaihy, Hosam; Chalmers, Stuart; Preece, Alun; Bar-Noy, Amotz; Porta, Thomas La

doi:10.1117/12.799859

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…In [3,4], the problem is formulated, with the objective of maximizing the number of applications to run. The same problem is investigated in [5] and [6], with the objectives of maximizing profit and maximizing satisfaction rate of tasks, respectively. These works are of little relevance to our work since they do not consider node level virtualization-enabled WSNs.…”

Section: A Static Task Assignmentmentioning

confidence: 99%

Energy Efficient Task Assignment in Virtualized Wireless Sensor Networks

Raee

Naboulsi

Glitho

2018

2018 IEEE Symposium on Computers and Communications (ISCC)

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Wireless Sensor Networks (WSNs) are being used extensively today in various domains. However, they are traditionally deployed with applications embedded in them which precludes their re-use for new applications. Nowadays, virtualization enables several applications on a same WSN by abstracting the physical resources (i.e. sensing capabilities) into logical ones. However, this comes at a cost, including an energy cost. It is therefore critical to ensure the efficient allocation of these resources. In this paper, we study the problem of assigning application sensing tasks to sensor devices, in virtualized WSNs. Our goal is to minimize the overall energy consumption resulting from the assignment. We focus on the static version of the problem and formulate it using Integer Linear Programming (ILP), while accounting for sensor nodes' available energy and virtualization overhead. We solve the problem over different scenarios and compare the obtained solution to the case of a traditional WSN, i.e. one with no support for virtualization. Our results show that significant energy can be saved when tasks are appropriately assigned in a WSN that supports virtualization.

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Section: A Static Task Assignmentmentioning

confidence: 99%

Energy Efficient Task Assignment in Virtualized Wireless Sensor Networks

Raee

Naboulsi

Glitho

2018

2018 IEEE Symposium on Computers and Communications (ISCC)

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“…In a different setting, Pizzocaro et al try to optimize a sensor mission assignment problem by modelling it as a NP-complete multiple knapsack problem [31]. They introduce the sensor utility maximization model in which each sensor-mission pair is associated with a utility offer.…”

Section: Related Workmentioning

confidence: 99%

An Agent-Based Distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming

Kho

Tran-Thanh

Rogers

et al. 2009

The Computer Journal

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The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding, and routing actions of each node in order to maximise the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each node's energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal agent-based decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting, and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose, and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared to the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured.

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Two stage decision making approach for Sensor Mission Assignment Problem

Rathi¹,

Balamohan²

2016

RAIRO-Oper. Res.

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Sensor Mission Assignment (SMA) is the process of assigning sensors to missions in the best way, which may depend on the cost of using individual sensors and the requirements of individual missions. SMA is Np-complete and is a special case of Generalized Assignment Problem. The significant bottlenecks in SMA are energy conservation and uncertainty in the demand of the missions. In order to conserve energy, some sensors are considered to be in sleeping state while others remain active for sensing. In this paper, Sensor Mission Assignment problem is studied in the context of Generalized Assignment Problem combined with decision making approach. Two stage decision making approach is formulated to determine the minimum number of sleeping sensors to be activated so as to assign exactly one sensor to each mission optimally subject to some of the energy resource constraints and environmental constraints imposed on it. The method draws upon the existing generalized assignment problem and the decision making approaches by analyzing trade-offs among desirable value of objective function and the constraints that include all the parameters. The proposed method is applied to the simulation on a small sized wireless sensor network and it is shown that the method is energy efficient. The proposed method provides more holistic point of view on the factors impacting sensor mission assignment.

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A knapsack approach to sensor-mission assignment with uncertain demands

Cited by 5 publications

References 8 publications

Energy Efficient Task Assignment in Virtualized Wireless Sensor Networks

Energy Efficient Task Assignment in Virtualized Wireless Sensor Networks

An Agent-Based Distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming

Two stage decision making approach for Sensor Mission Assignment Problem

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