A detailed review of energy-efficient medium access control protocols for mobile sensor networks

Ngo, Vincent; Anpalagan, Alagan

doi:10.1016/j.compeleceng.2009.03.005

Cited by 24 publications

(15 citation statements)

References 21 publications

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“…In [19] it has been demonstrated how a mobile robot, Miloš S. Stanković communicating with a base station, can exploit the effects of multi-path fading by performing small deviations from its predefined path. In [20] and references therein, some attempts are presented to include possibility of nodes' mobility and reconfigurability in order to improve energy efficiency of MAC protocol. Connectivity control in mobile sensor networks has been analyzed intensively in the existing literature from different points of view (e.g., [10] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Distributed mobility and power control for noncooperative robotic ad hoc and sensor networks

Stanković

Johansson

2011

IEEE Conference on Decision and Control and European Control Conference

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In this paper we propose novel algorithms for noncooperative power and position control in mobile ad hoc networks. The algorithms are distributed and adaptive, i.e., they are able to deal with the agents' lack of knowledge about the environmental conditions and about the actions, positions and properties of the other agents, which is the essential challenge in these networks. The agents' cost functions consist of a term proportional to the achievable rate of communication with the neighbors, explicitly depending on the interference from the other agents, and a pricing term penalizing excessive power (for the power control scheme) or deviation from predefined positions (for the position control scheme). We formulate conditions for the existence and uniqueness of the Nash equilibrium and prove that the algorithms converge to it almost surely, based only on local measurements and local signaling between the neighbors. The position control algorithm can be adopted to specific motion dynamics of the networked mobile robots. We illustrate the main properties of the algorithms through simulations.

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Section: Introductionmentioning

confidence: 99%

Distributed mobility and power control for noncooperative robotic ad hoc and sensor networks

Stanković

Johansson

2011

IEEE Conference on Decision and Control and European Control Conference

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“…They bring more difficulties and challenges such as lower energy efficiency, shorter lifetime, and higher data delivery delays 2 International Journal of Distributed Sensor Networks compared with WSNs [4]. Mobility also increases frame errors and the bit error rate (BER) due to low signal-tonoise ratio (SNR) [2]. Secondly, sensor nodes are tightly constrained in terms of energy, processing, and storage capacities.…”

Section: Introductionmentioning

confidence: 99%

“…However, many clustering protocols mainly focus on static sensor nodes or controlled mobility for hop-count reduction in data collection. Mobile sensor nodes are needed in applications where sensors are deployed on randomly moving objects for monitoring, tracking, and other purposes [2]. For example, wireless sensor devices have been attached to bikes, vehicles, and animals.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks

Liu

Ruan

2013

International Journal of Distributed Sensor Networks

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Random mobility and energy constraint are two main factors affecting system performance in mobile sensor networks, which cause many difficulties to system design. It is necessary to develop high-efficiency algorithms and protocols for mobile sensor networks to adapt to dynamic network environment and energy limitation. In this paper, a new clustering algorithm based on residual energy difference ratio is presented to improve system performance. Firstly, it is an energy-efficient algorithm. The residual energy of sensor nodes and average residual energy of system are considered in the residual energy difference ratio, which effectively avoid the nodes with low residual energy being selected as cluster heads. An energy-optimal scheme is used in cluster formation phase to minimize energy consumption. Secondly, it is a dynamic algorithm. The system dynamically clusters the sensor nodes according to the data transmission delays. It makes the whole system adapt to the random mobility of sensor nodes. The NS2 software is used to simulate the new clustering algorithm. The simulation experiments can verify the validity of the proposed theory.

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“…Equation (19) provides a mathematical relationship between the material potential at any oxidation time and each of the different experimental variables (temperature, electrolyte concentration, applied current).…”

Section: Results and Achievementsmentioning

confidence: 99%

“…where En(t) is given by equation (19) and pn(t) is the unitary pulse function, being pn(t)=1 if the n th electron is being extracted from every polymeric chain and pn(t)=0 if other electron different than n th is being extracted under current flow.…”

Section: Results and Achievementsmentioning

confidence: 99%

Conducting polymer actuators: from basic concepts to proprioceptive systems

Gil¹,

Gabriel²

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A detailed review of energy-efficient medium access control protocols for mobile sensor networks

Cited by 24 publications

References 21 publications

Distributed mobility and power control for noncooperative robotic ad hoc and sensor networks

Distributed mobility and power control for noncooperative robotic ad hoc and sensor networks

A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks

Conducting polymer actuators: from basic concepts to proprioceptive systems

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