## 1 Introduction  
The growth of the Internet of Things (IoT)-enabled services, including industrial automation, radar sensor data, e-healthcare, smart cities, vehicle routing control, smart homes, and so on, has led to a massive rise in the volume of data and allows access to information about the physical world [1, 2, 3, 4, 5, 6]. Because of the excessive separation between IoT objects and the cloud server, the traditional two-layered cloud-based IoT design suffers from significant data processing delays, making it unsuitable for delay-sensitive apps [7, 8]. To address this limitation and meet the demands of delay-sensitive applications, a three-layered IoT-edge-cloud structure has received a lot of attention. The edge platform provides cloud services at the network's edge to minimize the latency of processing activities performed by terminal nodes [9]. Edge computing decreases latency and the quantity of data transmitted to the cloud for processing, making it ideal for delay-sensitive IoT apps. This massive expansion necessitates systems to handle the growing number of IoT objects as well as create and communicate the generated mobile data [10; 11]. The devices' low power and processing capabilities, such as CPU and RAM, significantly limit their ability to run such resource-intensive apps [12]. To improve Quality of Service (QoS), particularly for IoT latency-sensitive applications, computation offloading is a strategy that transfers compute-intensive jobs or applications from the resource-constrained IoT devices to more processing-capable devices such as the cloud or edge. Therefore, delay-sensitive computations could be offloaded to more innovative platforms to overcome these restrictions and fulfill the communication/processing latency requirement. Additionally, Energy Harvesting (EH) is a promising way of extending the battery's life and supplying objects in a wide range of applications [13; 14; 15; 16]. IoT objects using EH components must resist eavesdroppers that analyze sensing data over radio channels to reveal the user's position and activities, such as the privacy of the use pattern [17; 18; 19; 20]. Therefore, EH techniques can be used by IoT systems to use environmental energy, such as body motion and ambient Radio Frequency (RF), to prolong battery life [21]. Furthermore, privacy is vital in offloading systems. Imagine the IoT system offloads all of the sensing data to the edge device while the radio channel is in excellent shape [22]. In such an instance, an attacker might identify the usage pattern by estimating the amount of the newly produced sensing data [23; 24]. Consequently, the IoT system must cover user location and usage pattern privacy in device offloading. Blockchain is used to initially record and store transactions as an underlying Bitcoin technology [25; 26]. Compared to a traditional centralized ledger managed by a third party, a blockchain is a distributed ledger with a Peer to Peer (P2P) network structure [27]. Therefore, it will guarantee the confidentiality and authenticity of data effectively. Regardless of whether it is a permissionless blockchain like Bitcoin or Ethereum, or an authorized blockchain like Byzantine Fault Tolerance (BFT), they all require a lot of mining or consensus computational resources [28]. Therefore, the reliability of blockchain computing and security are important concerns in the IoT [25; 29]. This research aims to create a low-energy-consumed short-delay computing system by combining IoT-edge, EH module, a blockchain platform, and Deep Learning (DL) technique [30]. To optimize latency, energy consumption, and battery life, we offer a novel version of the reinforcement learning approach to the Markov Decision Process (MDP) problem, making the most of the optimal decisions for online/offline offloading. When the state space is large, the MDP suffers from the well-known \"curse of dimensionality\" issue.