2,735 research outputs found
On Energy Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks
In this paper, a hierarchical cross-layer design approach is proposed to
increase energy efficiency in ad hoc networks through joint adaptation of
nodes' transmitting powers and route selection. The design maintains the
advantages of the classic OSI model, while accounting for the cross-coupling
between layers, through information sharing. The proposed joint power control
and routing algorithm is shown to increase significantly the overall energy
efficiency of the network, at the expense of a moderate increase in complexity.
Performance enhancement of the joint design using multiuser detection is also
investigated, and it is shown that the use of multiuser detection can increase
the capacity of the ad hoc network significantly for a given level of energy
consumption.Comment: To appear in the EURASIP Journal on Wireless Communications and
Networking, Special Issue on Wireless Mobile Ad Hoc Network
Network Lifetime Maximization With Node Admission in Wireless Multimedia Sensor Networks
Wireless multimedia sensor networks (WMSNs) are expected to support multimedia services such as delivery of video and audio streams. However, due to the relatively stringent quality-of-service (QoS) requirements of multimedia services (e.g., high transmission rates and timely delivery) and the limited wireless resources, it is possible that not all the potential sensor nodes can be admitted into the network. Thus, node admission is essential for WMSNs, which is the target of this paper. Specifically, we aim at the node admission and its interaction with power allocation and link scheduling. A cross-layer design is presented as a two-stage optimization problem, where at the first stage the number of admitted sensor nodes is maximized, and at the second stage the network lifetime is maximized. Interestingly, it is proved that the two-stage optimization problem can be converted to a one-stage optimization problem with a more compact and concise mathematical form. Numerical results demonstrate the effectiveness of the two-stage and one-stage optimization frameworks
Flow Allocation for Maximum Throughput and Bounded Delay on Multiple Disjoint Paths for Random Access Wireless Multihop Networks
In this paper, we consider random access, wireless, multi-hop networks, with
multi-packet reception capabilities, where multiple flows are forwarded to the
gateways through node disjoint paths. We explore the issue of allocating flow
on multiple paths, exhibiting both intra- and inter-path interference, in order
to maximize average aggregate flow throughput (AAT) and also provide bounded
packet delay. A distributed flow allocation scheme is proposed where allocation
of flow on paths is formulated as an optimization problem. Through an
illustrative topology it is shown that the corresponding problem is non-convex.
Furthermore, a simple, but accurate model is employed for the average aggregate
throughput achieved by all flows, that captures both intra- and inter-path
interference through the SINR model. The proposed scheme is evaluated through
Ns2 simulations of several random wireless scenarios. Simulation results reveal
that, the model employed, accurately captures the AAT observed in the simulated
scenarios, even when the assumption of saturated queues is removed. Simulation
results also show that the proposed scheme achieves significantly higher AAT,
for the vast majority of the wireless scenarios explored, than the following
flow allocation schemes: one that assigns flows on paths on a round-robin
fashion, one that optimally utilizes the best path only, and another one that
assigns the maximum possible flow on each path. Finally, a variant of the
proposed scheme is explored, where interference for each link is approximated
by considering its dominant interfering nodes only.Comment: IEEE Transactions on Vehicular Technolog
A survey of network lifetime maximization techniques in wireless sensor networks
Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri
Layering as Optimization Decomposition: Questions and Answers
Network protocols in layered architectures have historically been obtained on an ad-hoc basis, and much of the recent cross-layer designs are conducted through piecemeal approaches. Network protocols may instead be holistically analyzed and systematically designed as distributed solutions to some global optimization problems in the form of generalized Network Utility Maximization (NUM), providing insight on what they optimize and on the structures of network protocol stacks. In the form of 10 Questions and Answers, this paper presents a short survey of the recent efforts towards a systematic understanding of "layering" as "optimization decomposition". The overall communication network is modeled by a generalized NUM problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems. Furthermore, there are many alternative decompositions, each leading to a different layering architecture. Industry adoption of this unifying framework has also started. Here we summarize the current status of horizontal decomposition into distributed computation and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, power control, and coding. We also discuss under-explored future research directions in this area. More importantly than proposing any particular crosslayer design, this framework is working towards a mathematical foundation of network architectures and the design process of modularization
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Greening and Optimizing Energy Consumption of Sensor Nodes in the Internet of Things through Energy Harvesting: Challenges and Approaches
This paper presents a survey of current energy efficient technologies that could drive the IoT revolution while examining critical areas for energy improvements in IoT sensor nodes. The paper reviews improvements in emerging energy techniques which promise to revolutionize the IoT landscape. Moreover, the current work also studies the sources of energy consumption by the IoT sensor nodes in a network and the metrics adopted by various researchers in optimizing the energy consumption of these nodes. Increasingly, researchers are exploring better ways of sourcing sufficient energy along with optimizing the energy consumption of IoT sensor nodes and making these energy sources green. Energy harvesting is the basis of this new energy source. The harvested energy could serve both as the principal and alternative energy source of power and thus increase the energy constancy of the IoT systems by providing a green, sufficient and optimal power source among IoT devices. Communication of IoT nodes in a heterogeneous IoT network consumes a lot of energy and the energy level in the nodes depletes with time. There is the need to optimize the energy consumption of such nodes and the current study discusses this as well
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