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Register a new userMathematical Modelling of Energy Wastage in Absence of Levelling and Sectoring in Wireless Sensor Networks
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Priyanka Sharma
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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In this paper, we quantitatively (mathematically) reason the energy savings achieved by the Leveling and Sectoring protocol. Due to the energy constraints on the sensor nodes (in terms of supply of energy) energy awareness has become crucial in networking protocol stack. The understanding of routing protocols along with energy awareness in a network would help in energy opti-mization with efficient routing .We provide analytical modelling of the energy wastage in the absence of Leveling and Sectoring protocol by considering the network in the form of binary tree, nested tree and Q-ary tree. The simulation results reflect the energy wastage in the absence of Levelling and Sectoring based hybrid protocol.
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In this work, we propose a content caching and delivery strategy to maximize throughput capacity in cache-enabled wireless networks. To this end, efficient betweenness (EB), which indicates the ratio of content delivery paths passing through a node, is first defined to capture the impact of content caching and delivery on network traffic load distribution. Aided by EB, throughput capacity is shown to be upper bounded by the minimal ratio of successful delivery probability (SDP) to EB among all nodes. Through effectively matching nodes EB with their SDP, the proposed strategy improves throughput capacity with low computation complexity. Simulation results show that the gap between the proposed strategy and the optimal one (obtained through exhausted search) is kept smaller than 6%.
We integrate a wireless powered communication network with a cooperative cognitive radio network, where multiple secondary users (SUs) powered wirelessly by a hybrid access point (HAP) help a primary user relay the data. As a reward for the cooperation, the secondary network gains the spectrum access where SUs transmit to HAP using time division multiple access. To maximize the sum-throughput of SUs, we present a secondary sum-throughput optimal resource allocation (STORA) scheme. Under the constraint of meeting target primary rate, the STORA scheme chooses the optimal set of relaying SUs and jointly performs the time and energy allocation for SUs. Specifically, by exploiting the structure of the optimal solution, we find the order in which SUs are prioritized to relay primary data. Since the STORA scheme focuses on the sum-throughput, it becomes inconsiderate towards individual SU throughput, resulting in low fairness. To enhance fairness, we investigate three resource allocation schemes, which are (i) equal time allocation, (ii) minimum throughput maximization, and (iii) proportional time allocation. Simulation results reveal the trade-off between sum-throughput and fairness. The minimum throughput maximization scheme is the fairest one as each SU gets the same throughput, but yields the least SU sum-throughput.
The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.
Fog Radio Access Network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases. In contrast, this work focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs caches along with the delivery of the requested files. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit to the long-term NDT of F- RAN system. Analytical results are further verified by numerical simulation, yielding important design insights.
We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.
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