As the demand on artificial intelligence (AI)-based applications increases, the freshness of sensed data becomes crucial in the wireless sensor networks. Since those applications require a large amount of computation for processing the sensed data, it is essential to offload the computation load to the edge computing (EC) server. In this paper, we propose the sensing and computing decision (SCD) algorithms for data freshness in the EC-enabled wireless sensor networks. We define the η-coverage probability to show the probability of maintaining fresh data for more than η ratio of the network, where the spatial-temporal correlation of information is considered. We then propose the probability-based SCD for the single pre-charged sensor case with providing the optimal point after deriving the η-coverage probability. We also propose the reinforcement learning (RL)-based SCD by training the SCD policy of sensors for both the single pre-charged and multiple energy harvesting (EH) sensor cases, to make a real-time decision based on its observation. Our simulation results verify the performance of the proposed algorithms under various environment settings, and show that the RL-based SCD algorithm achieves higher performance compared to baseline algorithms for both the single pre-charged sensor and multiple EH sensor cases.

In this paper, we propose a joint sensing and computing decision algorithm for data freshness in edge computing (EC)-enabled wireless sensor networks. By introducing the data freshness at the presented networks, we define the$\eta$-coverage probability to show the probability of maintaining fresh data for more than$\eta$ratio of the network, where the spatial-temporal correlation of information is considered. To maximize the$\eta$-coverage probability in the networks with limited energy, we propose the reinforcement learning (RL)-based decision algorithm by training the policy of sensors. Our simulation results verify the performance of the proposed algorithm for different number of sensors and the computing energy. From the results, we show the proposed algorithm achieves higher$\eta$-coverage probability compared to the baseline algorithms.

The Age of Information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems, where timeliness is of importance. In this article, the problem of characterizing and controlling the AoI is studied in the context of blockchain-enabled monitoring networks (BeMNs). In BeMN, status updates from sources are transmitted and recorded in a blockchain. To investigate the statistical characteristics of the AoI in BeMN, the transmission latency and the consensus latency are first rigorously modeled. Then, the average AoI, the AoI violation probability, and the peak AoI violation probability are derived in a closed form so as to quantify the performance of BeMN. Furthermore, a simplified form is derived for the AoI violation probability, and it is shown that this quantity can capture the upper or lower bounds of the actual AoI violation probability. Simulation results show that each BeMN parameters (i.e., target successful transmission probability, block size, and timeout) can have conflicting effects on the AoI-related performance. Subsequently, design insights are provided to maintain the freshness of the status data in BeMN. Then, experimental results with a real Hyperledger Fabric platform further validate the accuracy of our modeling and analysis.

Age of information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems where timeliness is of importance. In this paper, we explore the data freshness in the Hyperledger Fabric Blockchain-enabled monitoring network (HeMN) by leveraging the AoI metric. In HeMN, status updates from sources are transmitted through an uplink and recorded in a Hyperledger Fabric (HLF) network. To provide a stochastic guarantee of data freshness, we derive a closed-form of AoI violation probability by considering the transmission latency and the consensus latency. Then, we validate our analytic results through the implemented HLF platform. We also investigate the effect of the target successful transmission probability (STP) on the AoI violation probability.

The mobile edge computing (MEC) has been introduced for providing computing capabilities at the edge of networks to improve the latency performance of wireless networks. In this paper, we provide the novel framework for MEC-enabled heterogeneous networks (HetNets), composed of the multi-tier networks with access points (APs) (i.e., MEC servers), which have different transmission power and different computing capabilities. In this framework, we also consider multiple-type mobile users with different sizes of computation tasks, and they offload the tasks to a MEC server, and receive the computation resulting data from the server. We derive the successful edge computing probability (SECP), defined as the probability that a user offloads and finishes its computation task at the MEC server within the target latency. We provide a closed-form expression of the approximated SECP for general case, and closed-form expressions of the exact SECP for special cases. This paper then provides the design insights for the optimal configuration of MEC-enabled HetNets by analyzing the effects of network parameters and bias factors, used in MEC server association, on the SECP. Specifically, it shows how the optimal bias factors in terms of SECP can be changed according to the numbers of user types and tiers of MEC servers, and how they are different to the conventional ones that did not consider the computing capabilities and task sizes.

The mobile edge computing (MEC) has been introduced for providing computing capabilities at the edge of networks to improve the latency performance of wireless networks. In this paper, we propose the novel framework for MEC-enabled heterogeneous networks (HetNets), composed of the multi-tier networks with access points (APs) having MEC servers. In this framework, the mobile users offload the different size of computation tasks to the MEC server having different computing capacities and transmission power, and receive the computation resulting data from the server. We derive the successful edge computing probability considering the successful computation and communication by using the queueing theory and stochastic geometry. We then provide how the bias factors in MEC server association and the network traffic affect the successful edge computing probability. This study provides the design insights for the optimal configuration of MEC-enabled networks.