Energy-efficient self-organizing communication protocols for wireless sensor networks

Block, Frederick J.; Baum, C.W.

doi:10.1109/milcom.2001.985820

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…These algorithms aim at minimizing the cost of the routing paths from each sensor node to the AP, where the cost of the path is defined as the sum or maximum of the costs of the links on that path. They are closely related to previous work on minimizing the cost per packet in [5,6,8,9,10].…”

Section: Distributed Energy Efficient Routingmentioning

confidence: 75%

“…None of these schemes, however, propose a routing protocol that uses this formulation. The effect of minimizing the cost per packet is investigated for different cost functions in [5,6,8,9,10]. The cost depends on the ratio of the initial battery energy to remaining battery energy in [5,6,10] or on the ratio of the rate of energy consumption to the remaining energy of the node in [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Energy efficient routing with delay guarantee for sensor networks

Coleri

Varaiya

2006

Wireless Netw

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The paper presents a routing algorithm that maximizes the lifetime of a sensor network in which all data packets are destined for a single collection node. Lifetime is maximized by adjusting the number of packets traversing each node. The adjustment is carried out by transmitting over alternative routes. The first part of the paper assumes that the worst case delay resulting from energy efficient routing is less than the maximum tolerable value. Ignoring the delay constraint of the network, the routes are selected as the solution to a linear programming (LP) problem in which the objective is to maximize the minimum lifetime of each node. The solution is implemented in a centralized algorithm, and then approximated by an iterative algorithm based on least cost path routing, in which each step is implemented efficiently in a distributed manner. The second part of the paper incorporates delay guarantee into energy efficient routing by constraining the length of the routing paths from each sensor node to the collection node. Simulations reveal that the lifetime of the network increases significantly by optimal routing, and including delay constraint in energy efficient routing improves the network performance since the delay of the network keeps increasing as the delay constraint is relaxed beyond the value at which the optimal lifetime is achieved.

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Section: Distributed Energy Efficient Routingmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Energy efficient routing with delay guarantee for sensor networks

Coleri

Varaiya

2006

Wireless Netw

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“…A WSN is a network formed by many sensors deployed within a sensing area, which have the ability to sense, wirelessly communicate, and process information. Sensors are not only able to sense and detect environmental targets and changes, but can also process collected data and send that processed data to a sink by wireless transmission [15]- [17]. In the sensing area, when a sensor node detects an event, it is referred to as a source node.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Two-Tier Data Dissemination Based on Q-Learning for Wireless Sensor Networks

Wang

Hsu

2020

IEEE Access

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Green communication for different kinds of wireless networks has begun to receive significant research attention recently. Green communication focuses mainly on the issue of improving energy efficiency substantially. A wireless sensor network (WSN) consists of a large number of randomly and widely deployed sensor nodes, and these nodes themselves have the ability to wireless communicate, detect and process data. Sensor nodes can thus detect their surrounding environment, and transmit related data to a sink via wireless communication. This study proposes two two-tier data dissemination schemes based on Q-learning for wireless sensor networks. In the proposed schemes, a source node uses Q-learning to find the most energy efficient data dissemination path from the source node to the sink. The first scheme is called TTDD-QL, and the second scheme is called TTDD-QL-A which is an advanced version of TTDD-QL. In TTDD-QL, the reward is determined by the distance between the current dissemination node and the sink. In each iteration, the proposed scheme will update the Q values. After multiple learning iterations, the Q values are converged, and the data dissemination path is found according to the Q values. In TTDD-QL-A, the reward is determined not only by the distance between the current dissemination node and the sink but also by the remaining energy of the current dissemination node. Simulation results show that TTDD-QL and TTDD-QL-A can reduce sensor node energy consumption and extend the lifetime of the WSN.

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“…The advantages, as well as some of the challenges that this new technology has to offer have been studied in [1], [2] and the references therein. In [3]- [5] the authors explore different communication protocols that can be used for such systems. How the network scales with respect to different measures is studied in [6], [7], while [8], [9] studies how to place the sensors to optimize the coverage of a region.…”

Section: Introductionmentioning

confidence: 99%

Balancing sensing and coverage in mobile sensor networks: A min-max approach

Caicedo-N

Žefran

2007

2007 46th IEEE Conference on Decision and Control

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This paper presents a min-max algorithm for assigning mobile robots in a mobile wireless sensor network to different tasks that the network must perform. The algorithm minimizes the maximum penalty that is imposed when one of the tasks is not fully attended. The particular problem that we study is how to uniformly distribute autonomous mobile sensing robots inside a convex region while also quickly locating one or more sensory sources that might appear inside the region. The limited sensing capabilities of each robot suggest that several agents should collaborate to locate each source. The issue is then how to assign a sufficient number of robots to the sensing task and move them towards the source (even though some of them might initially not sense it), thereby diminishing the coverage of the region. We show that the proposed algorithm converges towards a stable equilibrium point. The algorithm is fully distributed and thus scalable. Simulation results that verify the theoretical claims are also presented.

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Energy-efficient self-organizing communication protocols for wireless sensor networks

Cited by 13 publications

References 5 publications

Energy efficient routing with delay guarantee for sensor networks

Energy efficient routing with delay guarantee for sensor networks

Energy Efficient Two-Tier Data Dissemination Based on Q-Learning for Wireless Sensor Networks

Balancing sensing and coverage in mobile sensor networks: A min-max approach

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