542 research outputs found
TCP over low-power and lossy networks: tuning the segment size to minimize energy consumption
Low-power and Lossy Networks (LLNs), like wireless networks based upon the
IEEE 802.15.4 standard, have strong energy constraints, and are moreover
subject to frequent transmission errors, not only due to congestion but also to
collisions and to radio channel conditions. This paper introduces an analytical
model to compute the total energy consumption in an LLN due to the TCP
protocol. The model allows us to highlight some tradeoffs as regards the choice
of the TCP maximum segment size, of the Forward Error Correction (FEC)
redundancy ratio, and of the number of link-layer retransmissions, in order to
minimize the total energy consumption.Comment: TELECOM Bretagne Research Repor
Wireless industrial monitoring and control networks: the journey so far and the road ahead
While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks
Capacity of Cellular Wireless Network
Earlier definitions of capacity for wireless networks, e.g., transport or
transmission capacity, for which exact theoretical results are known, are well
suited for ad hoc networks but are not directly applicable for cellular
wireless networks, where large-scale basestation (BS) coordination is not
possible, and retransmissions/ARQ under the SINR model is a universal feature.
In this paper, cellular wireless networks, where both BS locations and mobile
user (MU) locations are distributed as independent Poisson point processes are
considered, and each MU connects to its nearest BS. With ARQ, under the SINR
model, the effective downlink rate of packet transmission is the reciprocal of
the expected delay (number of retransmissions needed till success), which we
use as our network capacity definition after scaling it with the BS density.
Exact characterization of this natural capacity metric for cellular wireless
networks is derived. The capacity is shown to first increase polynomially with
the BS density in the low BS density regime and then scale inverse
exponentially with the increasing BS density. Two distinct upper bounds are
derived that are relevant for the low and the high BS density regimes. A single
power control strategy is shown to achieve the upper bounds in both the
regimes. This result is fundamentally different from the well known capacity
results for ad hoc networks, such as transport and transmission capacity that
scale as the square root of the (high) BS density. Our results show that the
strong temporal correlations of SINRs with PPP distributed BS locations is
limiting, and the realizable capacity in cellular wireless networks in high-BS
density regime is much smaller than previously thought. A byproduct of our
analysis shows that the capacity of the ALOHA strategy with retransmissions is
zero.Comment: A shorter version to appear in WiOpt 201
Correlation-based Cross-layer Communication in Wireless Sensor Networks
Wireless sensor networks (WSN) are event based systems that rely on the collective effort of densely deployed sensor nodes continuously observing a physical phenomenon. The spatio-temporal correlation between the sensor observations and the cross-layer design advantages are significant and unique to the design of WSN. Due to the high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the energy-radiating physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. This unique characteristic of WSN can be exploited through a cross-layer design of communication functionalities to improve energy efficiency of the network.
In this thesis, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to capture the spatial and temporal correlations in WSN and to enable the development of efficient communication protocols. Based on this framework, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is described, which exploits the spatial correlation in the WSN in order to achieve efficient medium access. Furthermore, the cross-layer module (XLM), which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes, is developed. The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN. Finally, the cross-layer packet size optimization framework is described.Ph.D.Committee Chair: Ian F. Akyildiz; Committee Member: Douglas M. Blough; Committee Member: Mostafa Ammar; Committee Member: Raghupathy Sivakumar; Committee Member: Ye (Geoffrey) L
- …