15,150 research outputs found
A Cross-Layer Approach to Minimize the Energy Consumption in Wireless Sensor Networks
Energy efficiency represents one of the primary challenges in the development of wireless sensor networks (WSNs). Since communication is the most power consuming operation for a node, many current energy-efficient protocols are based on duty cycling mechanisms. However, most of these solutions are expensive from both the computational and the memory resources point of view and; therefore, they result in being hardly implementable on resources constrained devices, such as sensor nodes. This suggests to combine new communication protocols with hardware solutions able to further reduce the nodes' power consumption. In this work, a cross-layer solution, based on the combined use of a duty-cycling protocol and a new kind of active wake-up circuit, is presented and validated by using a test bed approach. The resulting solution significantly reduces idle listening periods by awakening the node only when a communication is detected. Specifically, an MAC scheduler manages the awakenings of a commercial power detector connected to the sensor node, and, if an actual communication is detected, it enables the radio transceiver. The effectiveness of the proposed cross-layer protocol has been thoroughly evaluated by means of tests carried out in an outdoor environment
An Energy-Efficient Cross-Layer approach for cloud wireless green communications
In wireless sensor networks (WSN), energy consumption is one of the crucial issues. It is very important to conserve energy at each sensor node to prolong a network lifetime. The main challenge in WSN is to develop an energy efficient algorithm to minimize energy consumption during transmitting information from deployed sensors up to the cloud resources. Many researches have been studied the designing of energy efficient technique based on one-layer stack model approach. In this study, we propose Energy-Efficient Cross-Layer (EECL) approach by using the interaction of MAC layer and physical layer information to be exploited by a network layer to achieve energy efficient communication. More precisely, network layer could utilize the MAC layer and physical layer information to establish an energy efficient route path to be used in forwarding data. The proposed EECL approach uses X-MAC protocol in support of duty cycle which introduces short preambles that switches to wake-up/sensing mode only for nodes belonging to routing path while the other nodes set to be in sleep mode. The distance between nodes that influences energy consumption and Bit Error Rate (BER) are set to be the parameters which they are help in indicating the required power for each hop during route path selection in WSN and avoid the rely-hops that suffering from high average BER and with farther distance. We conduct the experiment using Matlab to evaluate the effectiveness of our proposed approach in terms of power consumption and obtained data rate. The results show that our proposed EECL approach outperforms its representatives in the ability of tuning the power utilized in respect with required data rate that could satisfy the desired Quality-of-Service (QoS)
Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks
By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
An Energy Driven Architecture for Wireless Sensor Networks
Most wireless sensor networks operate with very limited energy sources-their
batteries, and hence their usefulness in real life applications is severely
constrained. The challenging issues are how to optimize the use of their energy
or to harvest their own energy in order to lengthen their lives for wider
classes of application. Tackling these important issues requires a robust
architecture that takes into account the energy consumption level of functional
constituents and their interdependency. Without such architecture, it would be
difficult to formulate and optimize the overall energy consumption of a
wireless sensor network. Unlike most current researches that focus on a single
energy constituent of WSNs independent from and regardless of other
constituents, this paper presents an Energy Driven Architecture (EDA) as a new
architecture and indicates a novel approach for minimising the total energy
consumption of a WS
Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)
In this paper, we use statistical tools to analysis dependency between
Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC).
Our approach has two main phases: profiling, and effective parameter
extraction. In former, a sensor network simulator is re-run 800 times with
different values for eight WSN parameters to profile consumed energy in nodes;
then in latter, three statistical analyses (p-value, linear and non-linear
correlation) are applied to the outcome of profiling phase to extract the most
effective parameters on WSN overall energy consumption.Comment: 5-pages. This paper has been accepted in PDCAT-2012 conference
(http://www.pdcat2012.org/
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