12 research outputs found
Age-of-Information Dependent Random Access for Massive IoT Networks
As the most well-known application of the Internet of Things (IoT), remote
monitoring is now pervasive. In these monitoring applications, information
usually has a higher value when it is fresher. A new metric, termed the age of
information (AoI), has recently been proposed to quantify the information
freshness in various IoT applications. This paper concentrates on the design
and analysis of age-oriented random access for massive IoT networks.
Specifically, we devise a new stationary threshold-based age-dependent random
access (ADRA) protocol, in which each IoT device accesses the channel with a
certain probability only when its instantaneous AoI exceeds a predetermined
threshold. We manage to evaluate the average AoI of the proposed ADRA protocol
mathematically by decoupling the tangled AoI evolution of multiple IoT devices
and modeling the decoupled AoI evolution of each device as a Discrete-Time
Markov Chain. Simulation results validate our theoretical analysis and affirm
the superior age performance of the proposed ADRA protocol over the
state-of-the-art age-oriented random access schemes.Comment: Accepted to appear at INFOCOM 2020 Workshop on Age of Informatio
Optimal Scheduling Policy for Minimizing Age of Information with a Relay
We consider IoT sensor network where multiple sensors are connected to
corresponding destination nodes via a relay. Thus, the relay schedules sensors
to sample and destination nodes to update. The relay can select multiple
sensors and destination nodes in each time. In order to minimize average
weighted sum AoI, joint optimization of sampling and updating policy of the
relay is investigated. For errorless and symmetric case where weights are
equally given, necessary and sufficient conditions for optimality is found.
Using this result, we obtain that the minimum average sum AoI in a closed-form
expression which can be interpreted as fundamental limit of sum AoI in a single
relay network. Also, for error-prone and symmetric case, we have proved that
greedy policy achieves the minimum average sum AoI at the destination nodes.
For general case, we have proposed scheduling policy obtained via reinforcement
learning.Comment: 30 page
Low-Power Random Access for Timely Status Update: Packet-based or Connection-based?
This paper investigates low-power random access protocols for timely status
update systems with age of information (AoI) requirements. AoI characterizes
information freshness, formally defined as the time elapsed since the
generation of the last successfully received update. Considering an extensive
network, a fundamental problem is how to schedule massive transmitters to
access the wireless channel to achieve low network-wide AoI and high energy
efficiency. In conventional packet-based random access protocols, transmitters
contend for the channel by sending the whole data packet. When the packet
duration is long, the time and transmit power wasted due to packet collisions
is considerable. In contrast, connection-based random access protocols first
establish connections with the receiver before the data packet is transmitted.
Intuitively, from an information freshness perspective, there should be
conditions favoring either side. This paper presents a comparative study of the
average AoI of packet-based and connection-based random access protocols, given
an average transmit power budget. Specifically, we consider slotted Aloha (SA)
and frame slotted Aloha (FSA) as representatives of packet-based random access
and design a request-then-access (RTA) protocol to study the AoI of
connection-based random access. We derive closed-form average AoI and average
transmit power consumption formulas for different protocols. Our analyses
indicate that the use of packet-based or connection-based protocols depends
mainly on the payload size of update packets and the transmit power budget. In
particular, RTA saves power and reduces AoI significantly, especially when the
payload size is large. Overall, our investigation provides insights into the
practical design of random access protocols for low-power timely status update
systems
Analysis of Slotted ALOHA with an Age Threshold
We present a comprehensive steady-state analysis of threshold-ALOHA, a
distributed age-aware modification of slotted ALOHA proposed in recent
literature. In threshold-ALOHA, each terminal suspends its transmissions until
the Age of Information (AoI) of the status update flow it is sending reaches a
certain threshold . Once the age exceeds , the terminal
attempts transmission with constant probability in each slot, as in
standard slotted ALOHA. We analyze the time-average expected AoI attained by
this policy, and explore its scaling with network size, . We derive the
probability distribution of the number of active users at steady state, and
show that as network size increases the policy converges to one that runs
slotted ALOHA with fewer sources: on average about one fifth of the users is
active at any time. We obtain an expression for steady-state expected AoI and
use this to optimize the parameters and , resolving the
conjectures in \cite{doga} by confirming that the optimal age threshold and
transmission probability are and , respectively. We find that
the optimal AoI scales with the network size as , which is almost half
the minimum AoI achievable with slotted ALOHA, while the loss from the maximum
throughput of remains below . We compare the performance of this
rudimentary algorithm to that of the SAT policy that dynamically adapts its
transmission probabilities
Age of Information in Ultra-Dense IoT Systems: Performance and Mean-Field Game Analysis
In this paper, a dense Internet of Things (IoT) monitoring system is
considered in which a large number of IoT devices contend for channel access so
as to transmit timely status updates to the corresponding receivers using a
carrier sense multiple access (CSMA) scheme. Under two packet management
schemes with and without preemption in service, the closed-form expressions of
the average age of information (AoI) and the average peak AoI of each device is
characterized. It is shown that the scheme with preemption in service always
leads to a smaller average AoI and a smaller average peak AoI, compared to the
scheme without preemption in service. Then, a distributed noncooperative medium
access control game is formulated in which each device optimizes its waiting
rate so as to minimize its average AoI or average peak AoI under an average
energy cost constraint on channel sensing and packet transmitting. To overcome
the challenges of solving this game for an ultra-dense IoT, a mean-field game
(MFG) approach is proposed to study the asymptotic performance of each device
for the system in the large population regime. The accuracy of the MFG is
analyzed, and the existence, uniqueness, and convergence of the mean-field
equilibrium (MFE) are investigated. Simulation results show that the proposed
MFG is accurate even for a small number of devices; and the proposed CSMA-type
scheme under the MFG analysis outperforms two baseline schemes with fixed and
dynamic waiting rates, with the average AoI reductions reaching up to 22% and
34%, respectively. Moreover, it is observed that the average AoI and the
average peak AoI under the MFE do not necessarily decrease with the arrival
rate.Comment: Fixed typos in Equations (4) and (7). 30 pages, 9 figure