We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capability. Every source node transmits a sequence of data packets to its destination using only the harvested energy. Each data packet is encoded with finite-length codewords, characterizing the nature of short codeword transmissions in random access networks. By combining tools from bulk-service Markov chains with stochastic geometry, we derive an analytical expression for the network average AoI and obtain closed-form results in two special cases, i.e., the small and large energy buffer size scenarios. Our analysis reveals the trade-off between energy accumulation time and transmission success probability. We then optimize the network average AoI by jointly adjusting the update rate and the blocklength of the data packet. Our findings indicate that the optimal update rate should be set to one in the energy-constrained regime where the energy consumption rate exceeds the energy arrival rate. This also means if the optimal blocklength of the data packet is pre-configured, an energy buffer size supporting only one transmission is sufficient.

Cybersickness detection is one of the primary tasks in Virtual Reality (VR) content production. The existing subjective and objective studies on cybersickness give few guiding implications to VR content creators. To do experimental verification on previous hypotheses and propose design guidelines, this paper investigates the relationship between cybersickness and postural behavior, by analyzing the surface electromyography (sEMG) signals and hand movement videos. We conducted a user study to build the sEMG-video Cybersickness Benchmark Dataset (sEMG-CBD) and employed statistical analysis to summarize the regular pattern of participants’ dizziness status under VR experiences. The results indicate that the fluctuations of cybersickness correlate positively with the extent of forearm sEMG signals and hand movements. The preliminary analysis implies the potentiality of sEMG-based cybersickness detection being used as one of the significant representations of VR viewing experience, which could contribute to VR content production.

We optimize the Age of Information (AoI) in random access networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. We derive analytical expressions for the transmission success probability and time-average AoI using stochastic geometry tools. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the time-average AoI. In addition, we establish a scaling law for the time-average AoI in random access networks, revealing that the optimal time-average AoI increases linearly with the deployment density. Notably, the growth rate under TSA is half of that under conventional slotted ALOHA.

We optimize the Age of Information (AoI) in mobile networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. Using stochastic geometry tools, we derive analytical expressions for the transmission success probability, mean peak AoI, and time-average AoI. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the mean peak and time-average AoI, respectively. In addition, we establish a scaling law for the mean peak AoI and time-average AoI in mobile networks, revealing that the optimal mean peak AoI and time-average AoI increase linearly with the deployment density. Notably, the growth rate of time-average AoI under TSA is half of that under SA. When considering the optimal mean peak AoI, the TSA protocol exhibits comparable performance to the traditional slotted ALOHA protocol. These findings conclusively affirm the advantage of TSA in reducing higher-order AoI, particularly in densely deployed networks.

In large-scale wireless networks, severe interference may incur that leads to the age of information (AoI) degradation. It is therefore important to study how to optimize the AoI performance. This paper focuses on the average AoI minimization in random access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the average AoI is derived, based on which the optimal average AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. We further compare the average AoI optimization with the peak AoI optimization. The comparison reveals that the optimal channel access probability for the average AoI optimization and the peak AoI optimization are the same. Yet, the optimal packet arrival rate for the average AoI optimization is smaller than that for the peak AoI optimization. The gap enlarges when the node deployment density becomes small.

For battery-limited IoT networks, the energy efficiency and Age of Information (AoI) are two key performance metrics. Yet the tradeoff between energy efficiency and AoI remains unclear for large-scale networks since the analysis becomes challenging due to the couple queue problem. This paper aims to address this issue by studying the performance limit of energy efficiency under AoI constraint.Specifically, we evaluate the energy efficiency via the expected number of successfully transmitted packets during each transmitter’s life time for which the explicit expression is derived based on the spatio-temporal analytical framework in [1]. By further taking the AoI constraint into consideration, explicit expressions of the Maximum Expected Number of Successfully Transmitted Packets (MENSTP) and the corresponding channel access probability are obtained. The analysis reveals that if the Power Ratio of the Transmission state and the Waiting state (PRTW) equals one, i.e., the energy consumption per time slot of the transmission state equals to that of the waiting state, then the expected number of successfully transmitted packets during each transmitter’s life time and the peak AoI can be optimized simultaneously; otherwise, the MENSTP declines with a stringent AoI constraint. Moreover, the performance gap enlarges when the PRTW or the node distribution density increases which reveals a crucial tradeoff between the energy efficiency and AoI. It is therefore of importance to properly tuning the channel access probability to strike an optimal energy-age tradeoff in battery-limited large-scale IoT networks.

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks with a large number of nodes, severe interference may incur that leads to Age-of-Information (AoI) degradation. It is, therefore, important to study how to optimize the AoI performance. This article focuses on the AoI minimization in random-access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on which the optimal peak AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. The analysis shows that when the channel access probability (resp., the packet arrival rate) is given, the optimal packet arrival rate (resp., the optimal channel access probability) is equal to one when nodes are sparsely deployed, and decreases as the node deployment density increases. With a joint tuning of these two system parameters, the optimal channel access probability always equals one. Moreover, with the sole tuning of the channel access probability, the optimal peak AoI is improved with a smaller packet arrival rate only when the node deployment density is high. In contrast, a higher channel access probability always improves peak AoI performance when the packet arrival rate is solely tuned. The analysis in this article sheds important light on freshness-aware design for large-scale networks.