35,651 research outputs found
Unified clustering and communication protocol for wireless sensor networks
In this paper we present an energy-efficient cross layer protocol for providing application specific reservations in wireless senor networks called the “Unified Clustering and Communication Protocol ” (UCCP). Our modular cross layered framework satisfies three wireless sensor network requirements, namely, the QoS requirement of heterogeneous applications, energy aware clustering and data forwarding by relay sensor nodes. Our unified design approach is motivated by providing an integrated and viable solution for self organization and end-to-end communication is wireless sensor networks. Dynamic QoS based reservation guarantees are provided using a reservation-based TDMA approach. Our novel energy-efficient clustering approach employs a multi-objective optimization technique based on OR (operations research) practices. We adopt a simple hierarchy in which relay nodes forward data messages from cluster head to the sink, thus eliminating the overheads needed to maintain a routing protocol. Simulation results demonstrate that UCCP provides an energy-efficient and scalable solution to meet the application specific QoS demands in resource constrained sensor nodes. Index Terms — wireless sensor networks, unified communication, optimization, clustering and quality of service
Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data
To overcome the limited coverage in traditional wireless sensor networks,
\emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To
achieve longer battery lives of user devices and incentive human involvement,
this paper presents a novel approach that seamlessly integrates MCS with
wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS),
for supporting crowd sensing with energy consumption and offering rewards as
incentives. The optimization problem is formulated to simultaneously maximize
the data utility and minimize the energy consumption for service operator, by
jointly controlling wireless-power allocation at the \emph{access point} (AP)
as well as sensing-data size, compression ratio, and sensor-transmission
duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the
optimal power allocation policy is shown to have a \emph{threshold}-based
structure with respect to a defined \emph{crowd-sensing priority} function for
each MS. Given fixed sensing-data utilities, the compression policy achieves
the optimal compression ratio. Extensive simulations are also presented to
verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206
Energy efficient cooperative computing in mobile wireless sensor networks
Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution
Energy optimization for wireless sensor networks using hierarchical routing techniques
Philosophiae Doctor - PhDWireless sensor networks (WSNs) have become a popular research area that is widely
gaining the attraction from both the research and the practitioner communities due to their
wide area of applications. These applications include real-time sensing for audio delivery,
imaging, video streaming, and remote monitoring with positive impact in many fields such
as precision agriculture, ubiquitous healthcare, environment protection, smart cities and
many other fields. While WSNs are aimed to constantly handle more intricate functions
such as intelligent computation, automatic transmissions, and in-network processing, such
capabilities are constrained by their limited processing capability and memory footprint as
well as the need for the sensor batteries to be cautiously consumed in order to extend their
lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by
proposing a novel clustering approach for routing the sensor readings in wireless sensor
networks. The main contribution of this dissertation is to 1) propose corrective measures to
the traditional energy model adopted in current sensor networks simulations that
erroneously discount both the role played by each node, the sensor node capability and
fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at
maximizing sensor networks lifetime. We propose three energy models for sensor network:
a) a service-aware model that account for the specific role played by each node in a sensor
network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These two models are complemented by a load balancing
model structured to balance energy consumption on the network of cluster heads
that forms the backbone for any cluster-based hierarchical sensor network. We present two
novel approaches for clustering the nodes of a hierarchical sensor network: a) a distanceaware
clustering where nodes are clustered based on their distance and the residual energy
and b) a service-aware clustering where the nodes of a sensor network are clustered
according to their service offered to the network and their residual energy. These
approaches are implemented into a family of routing protocols referred to as EOCIT
(Energy Optimization using Clustering Techniques) which combines sensor node energy
location and service awareness to achieve good network performance. Finally, building
upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on
Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed
as a novel multipath routing protocol that finds energy efficient routing paths for sensor
readings dissemination from the cluster heads to the sink/base station of a hierarchical
sensor network. Our simulation results reveal the relative efficiency of the newly proposed
approaches compared to selected related routing protocols in terms of sensor network
lifetime maximization
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Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs
Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method
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
Exploiting Interference for Efficient Distributed Computation in Cluster-based Wireless Sensor Networks
This invited paper presents some novel ideas on how to enhance the
performance of consensus algorithms in distributed wireless sensor networks,
when communication costs are considered. Of particular interest are consensus
algorithms that exploit the broadcast property of the wireless channel to boost
the performance in terms of convergence speeds. To this end, we propose a novel
clustering based consensus algorithm that exploits interference for
computation, while reducing the energy consumption in the network. The
resulting optimization problem is a semidefinite program, which can be solved
offline prior to system startup.Comment: Accepted for publication at IEEE Global Conference on Signal and
Information Processing (GlobalSIP 2013
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