In recent years, the increasing demand to see the status of objects over the Internet leads to an increase in the number of Internet of Things (IoT) applications. The unique nature of IoT, which involves potentially millions of interconnected devices with different data rate, power, bandwidth, and range specifications, requires different performance metrics than those conventionally employed in other communication applications. In conventional wireless communication systems such as cellular networks, performance indicators, including data rate and spectral efficiency, have become decisive, whereas in energy-constrained real-time IoT applications which require low data rate, the freshness of information has become a more prominent characteristic. Age of Information (AoI), which is the elapsed time after the last received packet update was created at the source, has emerged as a fundamental metric for determining the freshness of information and has attracted substantial research interest. In this regard, this article is dedicated to provide an overview of the current state of the art on the use of AoI for the design and optimization of a large variety IoT applications. After a brief introduction of the IoT and AoI fundamentals, this article presents a survey of the research works on common design issues, such as AoI-based optimization, scheduling for IoT networks, application of learning methods in large-scale IoT systems, real-life applications, and experimental results together with a synopsis of potential future applications and research challenges.

We investigate the timeliness in delivering updates within a multisource multihop Internet of Things (IoT) network via multicast transmissions with or without employing network coding, using a completely probabilistic model. Age of Information (AoI) is adopted to quantify the timeliness of packets. Extensive simulation results, which corroborate the theoretical findings, demonstrate that in scenarios where the number of sources is high, the number of intermediate nodes relaying to monitors is low, there are multiple monitors, the transmission success probability is low, and computational resources are sufficient, the utilization of network coding has a great potential to improve the data-freshness in multisource multihop IoT networks which closely represent the spine of the real-life scenarios.

We consider the timeliness in delivering an update consisting of multiple message packets to multiple users via multicast transmissions with or without employing network coding where Age of Information (AoI) is adopted to quantify the timeliness of packets. The expressions for peak and average expected AoIs are analytically derived for both uncoded and network coded transmissions for both 2-user and generalized$k$-users scenarios where the computational burden stemming from network coding is taken into account. The behavioral analyses of a number of network parameters are investigated, and the effect of data rate and computational capacity of nodes is analyzed. Simulations are performed for various Internet of Things (IoT) deployments, and the analyses suggest that the use of network coding for multicast transmissions can result in substantial AoI improvements, with the exception of scenarios in which sensors have extremely limited computational capabilities.

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the differential entropy of the input, output, and conditional mean. That is, the sum of differential entropies of the conditional mean and output is always greater than or equal to twice the input differential entropy. Various other properties such as upper bounds, asymptotics, Taylor series expansion, and connection to Fisher Information are obtained. An application of the lower bound in the remote-source coding problem is discussed, and extensions of the lower and upper bounds to the vector Gaussian channel are given.

In this article, we address the scheduling problem in wireless ad hoc networks by exploiting the computational advantage that comes when scheduling problems can be represented by claw-free conflict graphs where we consider a wireless broadcast medium. It is possible to formulate a scheduling problem of broadcast transmissions as finding the maximum weighted independent set (MWIS) in the conflict graph of the network. Finding the MWIS of a general graph is NP-hard leading to an NP-hard complexity of scheduling. In a claw-free conflict graph, MWIS may be found in polynomial time leading to a throughput-optimal scheduling. We show that the conflict graphs of certain wireless ad hoc networks are claw-free. In order to obtain claw-free conflict graphs in general networks, we suggest introducing additional conflicts (edges) with the aim of keeping the decrease in MWIS size minimal. To this end, we introduce an iterative optimization problem to decide where to introduce edges and investigate its efficient implementation. We conclude that the claw breaking method by adding extra edges can perform very close to optimal scenario and better than the polynomial time maximal independent set scheduling benchmark under the necessary assumptions.

In this paper, we address the resource allocation problem of device-to-device (D2D) communications underlaying orthogonal frequency division multiple access (OFDMA) based cellular systems by exploiting the efficiency that comes from an ensemble of graph theory and Knapsack problem. It is possible to construct the conflict graph of the D2D pairs by finding the maximal independent sets. Then, we use those independent sets as inputs to Knapsack problem iteratively in order to find D2D groups which allocate the subchannels. In Knapsack problem, we consider a maximum interference level that the base station is exposed at each subchannel. We illustrate that the proposed resource allocation method significantly outperforms graph coloring in terms of average data rate for the high number of underlaying D2D pairs in cellular systems.

We consider decentralized stochastic multi-armed bandit problem with multiple players in the case of different communication probabilities between players. Each player makes a decision of pulling an arm without cooperation while aiming to maximize his or her reward but informs his or her neighbors in the end of every turn about the arm he or she pulled and the reward he or she got. Neighbors of players are determined according to an Erdos-Rényi graph with connectivity α which is reproduced in the beginning of every turn. We consider i.i.d. rewards generated by a Bernoulli distribution and assume that players are unaware about the arms' probability distributions and their mean values. In case of a collision, we assume that only one of the players who is randomly chosen gets the reward where the others get zero reward. We study the effects of α, the degree of communication between players, on the cumulative regret using well-known algorithms UCB1, εGreedy and Thompson Sampling.

In this paper, our goal is to compare performances of three different algorithms to predict the ratings that will be given to movies by potential users where we are given a user-movie rating matrix based on the past observations. To this end, we evaluate User-Based Collaborative Filtering, Iterative Matrix Factorization and Yehuda Koren's Integrated model using neighborhood and factorization where we use root mean square error (RMSE) as the performance evaluation metric. In short, we do not observe significant differences between performances, especially when the complexity increase is considered. We can conclude that Iterative Matrix Factorization performs fairly well despite its simplicity.

In this paper, we address the scheduling problem in wireless ad hoc networks by exploiting the computational advantage that comes when such scheduling problems can be represented by claw-free conflict graphs. It is possible to formulate a scheduling problem of network coded flows as finding maximum weighted independent set (MWIS) in the conflict graph of the network. We consider activation of hyperedges in a hypergraph to model a wireless broadcast medium. We show that the conflict graph of certain wireless ad hoc networks are claw-free. It is known that finding MWIS of a general graph is NP-hard, but in a claw-free conflict graph, it is possible to apply Minty's or Faenza et al.'s algorithms in polynomial time. We discuss our approach on some sample networks.