The robustness of human social networks against epidemic propagation relies on the propensity for physical contact adaptation. During the early phase of infection, asymptomatic carriers exhibit the same activity level as susceptible individuals, which presents challenges for incorporating control measures in epidemic projection models. This article focuses on modeling and cost-efficient activity control of susceptible and carrier individuals in the context of the susceptible-carrier-infected-removed (SCIR) epidemic model over a two-layer contact network. In this model, individuals switch from a static contact layer to create new links in a temporal layer based on state-dependent activation rates. We derive conditions for the infection to die out or persist in a homogeneous network. Considering the significant costs associated with reducing the activity of susceptible and carrier individuals, we formulate an optimization problem to minimize the disease decay rate while constrained by a limited budget. We propose the use of successive geometric programming (SGP) approximation for this optimization task. Through simulation experiments on Poisson random graphs, we assess the impact of different parameters on disease prevalence. The results demonstrate that our SGP framework achieves a cost reduction of nearly 33% compared to conventional methods based on degree and closeness centrality.

Accurate and comprehensive data acquisition is critical for modern data-driven environmental applications. Mobile Crowdsensing (MCS) offers an effective approach by leveraging user participation to collect environmental data through task assignment. To minimize costs, MCS platforms often partition the environment into subareas and utilize inference algorithms to extrapolate data for entire subareas based on partial sensing in a limited subset. However, determining the optimal set of users for sensing tasks remains challenging due to constraints such as user availability and the complexity of data inference models. This paper introduces Sensify, a task assignment strategy that optimizes data acquisition by accounting for data correlations and budget constraints. Sensify efficiently selects subareas and recruits cost-effective users for sensing tasks, incorporating user-specific contexts such as location and device power availability during task assignment. To adaptively manage the platform budget, the strategy considers a dynamic set of users with varying costs over time. A deep recurrent reinforcement learning-based network is employed to select optimal subareas for sensing, while user recruitment is dynamically optimized using a reinforcement learning approach. Specifically, a modified Contextual Combinatorial Multi-Armed Bandit (CC-MAB) framework is utilized to handle the volatility and variability in user costs. Experiments conducted on two real-world datasets demonstrate that Sensify can improve data acquisition by up to 7% compared to existing approaches.

The temporal nature of data in Internet of Things (IoT) networks necessitates periodic updates of cached content at edge devices, while multicasting dynamic content can enhance network efficiency. This paper addresses the challenge of joint cache updating and multicast scheduling in a cache-enabled, queue-equipped small base station (SBS) with limited cache capacity, which accesses a macro base station (MBS) to download (update) uncached (cached) content and serves requests through multicasting. We formulate a two-stage optimization problem to minimize the average age of information (AAoI) per request, subject to constrained average queueing delay and access rate. The first stage employs the Lyapunov drift-plus-penalty method at the SBS to schedule multicasting and downloading (updating) uncached (cached) content. The second stage, implemented at the MBS, leverages deep reinforcement learning (DRL) to determine the content replacement policy. Simulation results show that the DRL-based cache replacement policy yields up to 50%, 59%, and 60% improvements in AAoI compared to the maximum age, least-recently-used, and least-frequently-used baseline policies, respectively.

Information aging has gained prominence in characterizing communication protocols for timely remote estimation and control applications. This work proposes an Age of Information (AoI)-aware threshold-based dynamic frame slotted ALOHA (T-DFSA) for contention resolution in random access machine-type communication networks. Unlike conventional DFSA that maximizes the throughput in each frame, the frame length and age-gain threshold in T-DFSA are determined to minimize the normalized average AoI reduction of the network in each frame. At the start of each frame in the proposed protocol, the common Access Point (AP) stores an estimate of the age-gain distribution of a typical node. Depending on the observedstatus of the slots, age-gains of successful nodes, and maximum available AoI, the AP adjusts its estimation in each frame. The maximum available AoI is exploited to derive the maximum possible age-gain at each frame and thus, to avoid overestimating the age-gain threshold, which may render T-DFSA unstable. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed T-DFSA compared to the existing optimal frame slotted ALOHA, threshold-ALOHA, and age-based thinning protocols in a considerable range of update generation rates.

Power management in multi-server data centers especially at scale is a vital issue of increasing importance in cloud computing paradigm. Existing studies mostly consider thresholds on the number of idle servers to switch the servers on or off and suffer from scalability issues. As a natural approach in view of the Markovian assumption, we present a multi-level continuous-time Markov decision process (CTMDP) model based on state aggregation of multi-server data centers with setup times that interestingly overcomes the inherent intractability of traditional MDP approaches due to their colossal state-action space. The beauty of the presented model is that, while it keeps loyalty to the Markovian behavior, it approximates the calculation of the transition probabilities in a way that keeps the accuracy of the results at a desirable level. Moreover, near-optimal performance is attained at the expense of the increased state-space dimensionality by tuning the number of levels in the multi-level approach. The simulation results were promising and confirm that in many scenarios of interest, the proposed approach attains noticeable improvements, namely a near 50% reduction in the size of CTMDP while yielding better rewards as compared to existing fixed threshold-based policies and aggregation methods.

Concurrent with the rise of real-time wireless systems enabled by the Internet of Things, the age of information (AoI) has been widely perceived as a crucial destination-centric performance metric to quantify the timeliness of data delivery. This paper deals with the analysis of information freshness in a multi-source M/G/1 queueing system with utilization of idle server time, referred to as server vacation, in an effective manner. In particular, the status update packets in our model are generated independently by a finite set of source nodes and according to a Poisson process, while their service time follows a general random variable. Using stochastic decomposition and the Laplace-Stieltjes transform, we derive the average AoI (AAoI) expression for the multi-source M/G/1 queueing model with generally-distributed vacation time in closed form. Our numerical simulations validate the accuracy of the derived AAoI expression and assess the impact of different parameters on the system performance.

Content caching is perceived as a promising approach to offload traffic from shared medium networks by pre-fetching and placing contents in caches during off-peak hours, and taking advantage of multicasting in the delivery times. While most existing efforts on caching are directed towards reducing the traffic rate over the shared medium, little attention is given to user preferences. Though placement based on the heterogeneity of content popularity improves the caching performance, it is understood to be a non-trivial combinatorial optimization problem. In this paper, we investigate the content caching problem under non-uniform and user-dependent demands and propose several heuristic methods by leveraging hybrid coded-uncoded caching, clustering, and the trade-off between local and global popularity of contents. Simulation results validate our analysis and show the out-performance of the proposed schemes with respect to existing methods.

In this paper, we aim to derive the average age of information (AAoI) associated with the static link scheduling policies in a buffer-aided two-way relay network, where two sources exchange their updates through an intermediate relay equipped with data buffers. With regard to buffer-aided relaying, we consider the two-mode relaying scheme, where each time slot is dedicated to either broadcast or multiple access mode. Moreover, the link scheduling policy is considered to be static, which randomizes between broadcast and multiple access modes when the relay is backlogged and chooses the multiple access mode, otherwise. We establish AAoI corresponding to the sources in terms of steady-state probabilities of buffers at the relay for different instants of the network. Through numerical results, we show that the joint status of the queues at the relay has a significant effect on determining the optimal static link scheduling policy and further validate our analytical approach via simulations.

In this paper, we use the concept of Markovian jump linear systems in order to analyze the age of information (AoI) in discrete-time Markovian systems. This approach is in fact, the discrete-time counterpart of the stochastic hybrid system (SHS) model reported for analyzing AoI in continuous=time Markovian systems and thus, is referred to as the discrete-time SHS (DT-SHS) model. We then apply the DT-SHS model in two wireless relay network settings to analyze and optimize the AoI associated with the static link scheduling policies. The first relay network comprises of one relay and a direct link between source and destination, whereas the second setting has two relays and no direct link. Moreover, a static link scheduling policy schedules the links without any knowledge about the state of the network. Using results obtained through numerical simulations, we validate our analytical approach and show the effect of relays in AoI improvement.

Content caching is a technique aimed to reduce the network load imposed by data transmission during peak time while ensuring users' quality of experience. Studies have shown that content delivery via coded caching can significantly improve beyond the performance limits of conventional caching schemes when caches and the server share a common link. Finding the optimal cache content placement however, becomes challenging under arbitrary distributions of content popularity. While existing works show that partitioning contents into three popularity levels performs better when multiple requests are received at each time slot, they neither delve into the problem analysis nor derive closed-form expressions for the optimum partitioning problem. In this paper, we analyze the coded caching scheme for a system with arbitrary content popularity, where we derive explicit closed-forms for the server load in the delivery phase and formulate the near-optimum partitioning problem. Simulation results are presented to corroborate our mathematical analysis.

In this paper, we derive optimal policies for cooperation of a wireless node in relaying the packets of a source node, in a random access environment. In our scenario, the source node sends its packets by its harvested energy and the relay node exploits its harvested energy in relaying the source packets not detected successfully at the destination. The relaying policies determine whether the relay node accepts or rejects the unsuccessfully transmitted source packets and how the relay node prioritizes the accepted source packets to its own packets. The optimization goal is to minimize the average transmission delay of source packets with and without a constraint on the average transmission delay of relay packets. We derive the optimal policies in static and dynamic formulations. In static one, optimum rejection and prioritization probabilities corresponding to the aforementioned aspects of relaying policies are derived. To this end, we model the status of the relay node by a quasi-birth-death process, derive the required parameters, and apply some numerical optimization methods. In dynamic formulation, the relay node optimally decides based on its status by employing a constrained Markov decision process. By numerical results, we show the efficiency of the optimal relaying policies in different conditions.

In this paper, we propose distributed static and dynamic optimal policies in a random access environment, comprised of energy harvesting (EH) nodes, in order to maximize the sum throughput. In static approach, each EH node exploits an optimal constant power to transmit its packets. However in dynamic one, the EH nodes adjust their transmission powers based on their network information, leading to exploit variable transmission powers. In static algorithm, the maximization is done through modeling energy buffer of EH nodes by a two-dimensional discrete time Markov chain which includes the effect of on-line charging and limited energy buffer. However, in dynamic approach, the variable power is allotted to EH nodes through modeling the problem as a Markov decision process. We observe that dynamic approach outperforms the static one by suitable management of collisions and available energy. Simulation results confirm our analytical approach.

In this paper, we evaluate the effect of energy constraints on the performance of a simple network comprised of wireless nodes with energy harvesting capability. In this scenario, wireless nodes contend with each other based on slotted Aloha protocol in order to transmit a packet. Packet transmission occurs provided that enough energy exists in the energy buffer. We propose an analytical model based on a closed queueing network (QN) to include details of data and energy buffers as well as random access MAC protocol. We show how energy limitation affects the MAC design parameters, e.g., contention window size, in order to optimize the performance of the network. Moreover, we evaluate the effect of energy constraints on maximum stable throughput, stability region, and packet drop rate. We also validate our analytical model by simulation.

In this paper, we focus on a cognitive network scenario, comprised of a WLAN overlaid on a network with time-scheduled primary users. WiMAX is an example of such primary networks. For time-scheduled primary users simple On-Off traffic model with exponential durations is not valid anymore. In this scenario, the cognitive nodes (CNs) hear downlink map (DL-MAP) and thus, know the frequency and time locations of all allocated slots at each frame. Then, cognitive nodes contend with each other in order to transmit their fixed size packets based on IEEE 802.11 MAC protocol. Since the number of empty slots at each frame is variable each packet transmission in cognitive network prolongs a random number of frames. By mapping the status of each CN on an open queueing network, including the details of the contention status with the other CNs as well as the statistical distribution of empty slots we are able to derive the saturation throughput of the cognitive WLAN. Finally, we derive the saturation throughput of cognitive network versus the number of CNs as well as the packet arrival rate at WiMAX in downlink direction and confirm our analytical results by simulation.

In this study, the authors propose an analytical model to evaluate the maximum stable throughput and average delay of a cognitive IEEE 802.11-based wireless local area network (WLAN) overlaid on uplink band of a cellular network. The main feature of the proposed model is to include different status of the cognitive users, that is, different spectrum opportunities for different secondary users, as the result of dynamic nature of primary users as well as different locations of the cognitive users relative to primary ones. The proposed model has been founded on an open queueing network comprised of several queueing nodes equivalent to different states of a typical secondary user. By mapping the details of MAC scheme of the cognitive WLAN and dynamic nature of spectrum opportunities onto suitable parameters of the proposed analytical model and writing the corresponding traffic equations, the authors are able to find the maximum stable throughput, that is, the maximum rate of packet generation at the cognitive nodes guaranteeing the stability of all nodes. Below this rate, that is, at the rate when all cognitive nodes are in non-saturation mode, with resort to the proposed analytical model the authors are able to evaluate the average delay comprised of the queueing delay as well as the transmission delay. The authors also show the applicability of our approach in evaluating the effect of different parameters of the cognitive network scenario, for example, the number of users, activity factors etc., onto the maximum stable throughput and non-saturation average delay. Simulation results confirm the validity of the authors analytical approach.

In this paper, we propose an analytical model to evaluate the maximum stable throughput of a cognitive IEEE 802.11-based WLAN sharing the downlink band of a cellular network. Our model has been found on an open queueing network which includes both asymmetric and non-saturated aspects of secondary nodes as well as the time-varying nature of the channel due to intermittent primary nodes. By mapping the details of MAC scheme of the cognitive WLAN, regarding the dynamic nature of spectrum opportunities, onto suitable parameters of the proposed queueing network and writing the corresponding traffic equations, we are able to find the maximum stable throughput, i.e., the maximum rate of packet generation at the cognitive nodes guaranteeing the stability of all nodes. Simulation results show the validity of our analytical approach.