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Optimal Radius for Connectivity in DutyCycled Wireless Sensor Networks ∗
"... We investigate the condition on transmission radius needed to achieve connectivity in dutycycled wireless sensor networks (briefly, DCWSN). First, we settle a conjecture of Das et. al. (2012) and prove that the connectivity condition on Random Geometric Graphs (RGG), given by Gupta and Kumar (1989 ..."
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We investigate the condition on transmission radius needed to achieve connectivity in dutycycled wireless sensor networks (briefly, DCWSN). First, we settle a conjecture of Das et. al. (2012) and prove that the connectivity condition on Random Geometric Graphs (RGG), given by Gupta and Kumar (1989), can be used to derive a weak sufficient condition to achieve connectivity in DCWSN. We also present a stronger result which gives a necessary and sufficient condition for connectivity and is hence optimal. The optimality of such a radius is also tested via simulation for two specific dutycycle schemes, called the contiguous and the random selection dutycycle scheme.
Article EnergyEfficient Algorithm for Multicasting in DutyCycled Sensor Networks
, 2015
"... Abstract: Multicasting is a fundamental network service for onetomany communications in wireless sensor networks. However, when the sensor nodes work in an asynchronous dutycycled way, the sender may need to transmit the same message several times to one group of its neighboring nodes, which comp ..."
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Abstract: Multicasting is a fundamental network service for onetomany communications in wireless sensor networks. However, when the sensor nodes work in an asynchronous dutycycled way, the sender may need to transmit the same message several times to one group of its neighboring nodes, which complicates the minimum energy multicasting problem. Thus, in this paper, we study the problem of minimum energy multicasting with adjusted power (the MEMAP problem) in the dutycycled sensor networks, and we prove it to be NPhard. To solve such a problem, the concept of an auxiliary graph is proposed to integrate the scheduling problem of the transmitting power and transmitting time slot and the constructing problem of the minimum multicast tree in MEMAP, and a greedy algorithm is proposed to construct such a graph. Based on the proposed auxiliary graph, an approximate scheduling and constructing algorithm with an approximation ratio of 4lnK is proposed, where K is the number of destination nodes. Finally, the theoretical analysis and experimental results verify the efficiency of the proposed algorithm in terms of the energy cost and transmission redundancy.
1Compressed Data Aggregation: Energy Efficient and High Fidelity Data Collection
"... Abstract—We focus on wireless sensor networks (WSNs) that perform data collection with the objective of obtaining the whole data set at the sink (as oppose to a function of the data set). In this case, energy efficient data collection requires the use of data aggregation. Whereas many data aggregati ..."
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Abstract—We focus on wireless sensor networks (WSNs) that perform data collection with the objective of obtaining the whole data set at the sink (as oppose to a function of the data set). In this case, energy efficient data collection requires the use of data aggregation. Whereas many data aggregation schemes have been investigated, they either compromise the fidelity of the recovered data or require complicated innetwork compressions. In this paper, we propose a novel data aggregation scheme that exploits compressed sensing (CS) to achieve both recovery fidelity and energy efficiency in WSNs with arbitrary topology. We make use of diffusion wavelets to find a sparse basis that characterizes the spatial (and temporal) correlations well on arbitrary WSNs, which enables straightforward CSbased data aggregation as well as high fidelity data recovery at the sink. Based on this scheme, we investigate the minimum energy compressed data aggregation problem. We first prove its NPcompleteness, and then propose a mixed integer programming formulation along with a greedy heuristic to solve it. We evaluate our scheme by extensive simulations on both real datasets and synthetic datasets. We demonstrate that our compressed data aggregation scheme is capable of delivering data to the sink with high fidelity while achieving significant energy saving. Index Terms—Wireless sensor networks (WSNs), data collection, data aggregation, compressed sensing (CS), diffusion wavelets, energy efficiency I.
FAVOR: Frequency Allocation for Versatile Occupancy of spectRum in Wireless Sensor Networks
"... While the increasing scales of the recent WSN deployments keep pushing a higher demand on the network throughput, the 16 orthogonal channels of the ZigBee radios are intensively explored to improve the parallelism of the transmissions. However, the interferences generated by other ISM band wireles ..."
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While the increasing scales of the recent WSN deployments keep pushing a higher demand on the network throughput, the 16 orthogonal channels of the ZigBee radios are intensively explored to improve the parallelism of the transmissions. However, the interferences generated by other ISM band wireless devices (e.g., WiFi) have severely limited the usable channels for WSNs. Such a situation raises a need for a spectrum utilizing method more efficient than the conventional multichannel access. To this end, we propose to shift the paradigm from discrete channel allocation to continuous frequency allocation in this paper. Motivated by our experiments showing the flexible and efficient use of spectrum through continuously tuning channel center frequencies with respect to link distances, we present FAVOR (Frequency Allocation for Versatile Occupancy of spectRum) to allocate proper center frequencies in a continuous spectrum (hence potentially overlapped channels, rather than discrete orthogonal channels) to nodes or links. To find an optimal frequency allocation, FAVOR creatively combines location and frequency into one space and thus transforms the frequency allocation problem into a spatial tessellation problem. This allows FAVOR to innovatively extend a spatial tessellation technique for the purpose of frequency allocation. We implement FAVOR in MicaZ platforms, and our extensive experiments with different network settings strongly demonstrate the superiority of FAVOR over existing approaches.