3,017 research outputs found
Smartening the Environment using Wireless Sensor Networks in a Developing Country
The miniaturization process of various sensing devices has become a reality
by enormous research and advancements accomplished in Micro Electro-Mechanical
Systems (MEMS) and Very Large Scale Integration (VLSI) lithography. Regardless
of such extensive efforts in optimizing the hardware, algorithm, and protocols
for networking, there still remains a lot of scope to explore how these
innovations can all be tied together to design Wireless Sensor Networks (WSN)
for smartening the surrounding environment for some practical purposes. In this
paper we explore the prospects of wireless sensor networks and propose a design
level framework for developing a smart environment using WSNs, which could be
beneficial for a developing country like Bangladesh. In connection to this, we
also discuss the major aspects of wireless sensor networks.Comment: 5 page
A reliable and resource aware framework for data dissemination in wireless sensor networks
Distinctive from traditional wireless ad hoc networks, wireless sensor networks (WSN) comprise a large number of low-cost miniaturized nodes each acting autonomously and equipped with short-range wireless communication mechanism, limited memory, processing power, and a physical sensing capability. Since sensor networks are resource constrained in terms of power, bandwidth and computational capability, an optimal system design radically changes the performance of the sensor network. Here, a comprehensive information dissemination scheme for wireless sensor networks is performed. Two main research issues are considered: (1) a collaborative flow of information packet/s from the source to sink and (2) energy efficiency of the sensor nodes and the entire system. For the first issue, we designed and evaluated a reactive and on-demand routing paradigm for distributed sensing applications. We name this scheme as IDLF-Information Dissemination via Label ForwarDing IDLF incorporates point to point data transmission where the source initiates the routing scheme and disseminates the information toward the sink (destination) node. Prior to transmission of actual data packet/s, a data tunnel is formed followed by the source node issuing small label information to its neighbors locally. These labels are in turn disseminated in the network. By using small size labels, IDLF avoids generation of unnecessary network traffic and transmission of duplicate packets to nodes. To study the impact of node failures and to improve the reliability of the network, we developed another scheme which is an extension to IDLF. This new scheme, RM-IDLF - Reliable Multipath Information dissemination by Label Forwarding, employ an alternate disjoint path. This alternate path scheme (RM-IDLF) may have a higher path cost in terms of energy consumption, but is more reliable in terms of data packet delivery to sink than the single path scheme (IDLF). In the latter scheme, the protocol establishes multiple (alternate) disjoint path/s from source to destination with negligible control overhead to balance load due to heavy data traffic among intermediate nodes from source to the destination. Another point of interest in this framework is the study of trade-offs between the achieved routing reliability using multiple disjoint path routing and extra energy consumption due to the use of additional path/s. Also, the effect of the failed nodes on the network performance is evaluated within the sensor system; Performance of the label dissemination scheme is evaluated and compared with the classic flooding and SPIN. (Abstract shortened by UMI.)
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Optimizing the beacon exchange rate for proactive autonomic configuration in ubiquitous MANETs
Proactive self-configuration is indispensable for MANETs like ubiquitous sensor networks (USNs), as component devices of the network are usually exposed to natural or man-made disasters due to the hostile deployment and ad hoc nature of the USNs. Network state beacons (NSBs) are exchanged among the key nodes of the network for crucial and effective monitoring of the network for steady state operation. The rate of beacon exchange (F/sub E/) and its contents, define the time and nature of the proactive action. Therefore it is very important to optimize these parameters to tune the functional response of the USN. This paper presents a comprehensive model for monitoring and proactively reconfiguring the network by optimizing the F/sub E/. The results confirm the improved throughput while maintaining QoS over longer periods of network operation
Energy Optimal Data Propagation in Wireless Sensor Networks
We propose an algorithm which produces a randomized strategy reaching optimal
data propagation in wireless sensor networks (WSN).In [6] and [8], an energy
balanced solution is sought using an approximation algorithm. Our algorithm
improves by (a) when an energy-balanced solution does not exist, it still finds
an optimal solution (whereas previous algorithms did not consider this case and
provide no useful solution) (b) instead of being an approximation algorithm, it
finds the exact solution in one pass. We also provide a rigorous proof of the
optimality of our solution.Comment: 19 page
Stochastic Models and Adaptive Algorithms for Energy Balance in Sensor Networks
We consider the important problem of energy balanced data propagation in wireless sensor networks and we extend and generalize previous works by allowing adaptive energy assignment. We consider the data gathering problem where data are generated by the sensors and must be routed toward a unique sink. Sensors route data by either sending the data directly to the sink or in a multi-hop fashion by delivering the data to a neighbouring sensor. Direct and neighbouring transmissions require different levels of energy consumption. Basically, the protocols balance the energy consumption among the sensors by computing the adequate ratios of direct and neighbouring transmissions. An abstract model of energy dissipation as a random walk is proposed, along with rigorous performance analysis techniques. Two efficient distributed algorithms are presented and analyzed, by both rigorous means and simulation. The first one is easy to implement and fast to execute. The protocol assumes that sensors know a-priori the rate of data they generate. The sink collects and processes all these information in order to compute the relevant value of the protocol parameter. This value is transmitted to the sensors which individually compute their optimal ratios of direct and neighbouring transmissions. The second protocol avoids the necessary a-priori knowledge of the data rate generated by sensors by inferring the relevant information from the observation of the data paths. Furthermore, this algorithm is based on stochastic estimation methods and is adaptive to environmental change
Development of mobile agent framework in wireless sensor networks for multi-sensor collaborative processing
Recent advances in processor, memory and radio technology have enabled production of tiny, low-power, low-cost sensor nodes capable of sensing, communication and computation. Although a single node is resource constrained with limited power, limited computation and limited communication bandwidth, these nodes deployed in large number form a new type of network called the wireless sensor network (WSN). One of the challenges brought by WSNs is an efficient computing paradigm to support the distributed nature of the applications built on these networks considering the resource limitations of the sensor nodes. Collaborative processing between multiple sensor nodes is essential to generate fault-tolerant, reliable information from the densely-spatial sensing phenomenon. The typical model used in distributed computing is the client/server model. However, this computing model is not appropriate in the context of sensor networks. This thesis develops an energy-efficient, scalable and real-time computing model for collaborative processing in sensor networks called the mobile agent computing paradigm. In this paradigm, instead of each sensor node sending data or result to a central server which is typical in the client/server model, the information processing code is moved to the nodes using mobile agents. These agents carry the execution code and migrate from one node to another integrating result at each node. This thesis develops the mobile agent framework on top of an energy-efficient routing protocol called directed diffusion. The mobile agent framework described has been mapped to collaborative target classification application. This application has been tested in three field demos conducted at Twentynine palms, CA; BAE Austin, TX; and BBN Waltham, MA
Wireless communication protocol architectures for nanosensor networks
Thesis (M.S.) University of Alaska Fairbanks, 2004Recent developments in micro fabrication and nanotechnology will enable the inexpensive manufacturing of massive numbers of tiny computing elements with sensors. New programming paradigms are required to obtain organized and coherent behavior from the cooperation of large numbers of sensor nodes. The individual nodes are identical, randomly placed and unreliable. They communicate with a small local neighborhood via wireless broadcast. In such environments, where individual nodes have limited resources, aggregating the node into groups is useful for specialization, increased robustness, and efficient resource allocation. In this paper, an application-specific self-organization protocol stack is developed. The clustering process is divided into phases. The first phase is to know the neighbor nodes. The second phase is to set up the cluster and routing. A 'find maximum clique algorithm' is used to set up clusters. A back off method is used to set up the hop field and routing. Group leaders set up a TDMA schedule for steady state operation. This schedule ensures that there is no conflict among in the same cluster and between clusters. Direct-sequence spread spectrum (DS-SS) is used to avoid inter-group conflict. The limited power resource is a challenge in nanosensor networks. This paper uses two different ways to analyze energy consumed in nanosensor networks, energy cost field and bit flow method. Sensor node deployment, cluster size, and propagation condition effect are discussed in this paper by those two methods respectively
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