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Register a new userAge of Information Scaling in Large Networks with Hierarchical Cooperation
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Added by
Baturalp Buyukates
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Given $n$ randomly located source-destination (S-D) pairs on a fixed area network that want to communicate with each other, we study the age of information with a particular focus on its scaling as the network size $n$ grows. We propose a three-phase transmission scheme that utilizes textit{hierarchical cooperation} between users along with textit{mega update packets} and show that an average age scaling of $O(n^{alpha(h)}log n)$ per-user is achievable where $h$ denotes the number of hierarchy levels and $alpha(h) = frac{1}{3cdot2^h+1}$ which tends to $0$ as $h$ increases such that asymptotically average age scaling of the proposed scheme is $O(log n)$. To the best of our knowledge, this is the best average age scaling result in a status update system with multiple S-D pairs.
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We consider the age of information in a multihop multicast network where there is a single source node sending time-sensitive updates to $n^L$ end nodes, and $L$ denotes the number of hops. In the first hop, the source node sends updates to $n$ first-hop receiver nodes, and in the second hop each first-hop receiver node relays the update packets that it has received to $n$ further users that are connected to it. This network architecture continues in further hops such that each receiver node in hop $ell$ is connected to $n$ further receiver nodes in hop $ell+1$. We study the age of information experienced by the end nodes, and in particular, its scaling as a function of $n$. We show that, using an earliest $k$ transmission scheme in each hop, the age of information at the end nodes can be made a constant independent of $n$. In particular, the source node transmits each update packet to the earliest $k_1$ of the $n$ first-hop nodes, and each first-hop node that receives the update relays it to the earliest $k_2$ out of $n$ second-hop nodes that are connected to it and so on. We determine the optimum $k_ell$ stopping value for each hop $ell$ for arbitrary shifted exponential link delays.
We consider the age of information in a multicast network where there is a single source node that sends time-sensitive updates to $n$ receiver nodes. Each status update is one of two kinds: type I or type II. To study the age of information experienced by the receiver nodes for both types of updates, we consider two cases: update streams are generated by the source node at-will and update streams arrive exogenously to the source node. We show that using an earliest $k_1$ and $k_2$ transmission scheme for type I and type II updates, respectively, the age of information of both update streams at the receiver nodes can be made a constant independent of $n$. In particular, the source node transmits each type I update packet to the earliest $k_1$ and each type II update packet to the earliest $k_2$ of $n$ receiver nodes. We determine the optimum $k_1$ and $k_2$ stopping thresholds for arbitrary shifted exponential link delays to individually and jointly minimize the average age of both update streams and characterize the pareto optimal curve for the two ages.
We consider a network consisting of a single source and $n$ receiver nodes that are grouped into $m$ equal size communities, i.e., clusters, where each cluster includes $k$ nodes and is served by a dedicated cluster head. The source node kee
We study age of information in a status updating system that consists of a single sampler, i.e., source node, that sends time-sensitive status updates to a single monitor node through a server node. We first consider a Gilbert-Elliot service profile at the server node. In this model, service times at the server node follow a finite state Markov chain with two states: ${bad}$ state $b$ and ${good}$ state $g$ where the server is faster in state $g$. We determine the time average age experienced by the monitor node and characterize the age-optimal state transition matrix $P$ with and without an average cost constraint on the service operation. Next, we consider a Gilbert-Elliot sampling profile at the source. In this model, the interarrival times follow a finite state Markov chain with two states: ${bad}$ state $b$ and ${good}$ state $g$ where samples are more frequent in state $g$. We find the time average age experienced by the monitor node and characterize the age-optimal state transition matrix $P$.
In this paper, we introduce a network-decomposed hierarchical cooperation (HC) protocol and completely characterize the corresponding throughput--delay trade-off for a large wireless ad hoc network formed in the context of social relationships. Instead of randomly picking source--destination pairings, we first consider a distance-based social formation model characterized by the social group density $gamma$ and the number of social contacts per node, $q$, where the probability that any two nodes in distance $d$ away from each other are socially connected is assumed to be proportional to $d^{-gamma}$, which is a feasible scenario. Then, using muiltihop and network-decomposed HC protocols under our social formation model, we analyze a generalized throughput--delay trade-off according to the operating regimes with respect to parameters $gamma$ and $q$ in both a dense network of unit area and an extended network of unit node density via a non-straightforward network transformation strategy. Our main results reveal that as $gamma$ increases, performance on the throughput--delay trade-off can remarkably be improved, compared to the network case with no social relationships. It is also shown that in the dense network, the network-decomposed HC protocol always outperforms the multihop protocol, while the superiority of the network-decomposed HC depends on $gamma$ and the path-loss exponent in the extended network.
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