## 1 Introduction  
The Internet of Things (IoT) is an ecosystem that interconnects \"smart\" objects that are connected to the internet [1]. These objects sense data from the environment and route it to the internet to be collected, processed, and analyzed using technologies [2]. The IoT architecture is generally composed of three layers: the application layer, the network layer, and the perception layer. The perception layer has all of the physical objects that interconnect together and form a network [3]. Wireless Sensor Networks (WSN) are considered one of the main enabling technologies for this layer [3,4]. They are composed of low-power sensor nodes that have limited computational and storing capabilities and a powerful base station (BS) [5]. Wireless sensor networks are used in a wide range of applications, such as environmental monitoring and control, health care systems, office automation, and home control [3,6]. In these applications, sensors share their data through the shared wireless medium [3]. Due to this reason and the limitation detailed earlier, they can be vulnerable to multiple numbers of security attacks that can affect the integrity and confidentiality of the data, such as a packet drop, data modification, a packet replay, and false data injections [7,8]. By injecting false data into the network, the attacker can make fake reports that will lead to serious damage or dangerous effects. Therefore, it is very crucial to have a countermeasure to filter false data as early and accurately as possible. Some mechanisms have been developed to secure the IoT from such attacks. These solutions vary regarding techniques [1]. Some of these solutions use software attestation, anomaly detection techniques, trust-based techniques, signature-based techniques, or watermarking techniques. However, most of these mechanisms have their limitations and cannot be used effectively to provide both confidentiality and integrity. Software attestation techniques require special hardware. The anomaly detection and trust-based techniques can suffer from a high rate of false positives [9]. They also involve extra steps of building a distribution of the normal behavior first. Signature-based techniques can be considered computationally complex [10]. Watermarking techniques are used in many applications. They were originally used to protect multimedia content and relational databases [11]. Many researchers have used them to secure sensory data. In the watermarking process, each datum is embedded with a unique watermark by the sensor node before sending it. The access point can then verify its integrity. The above weaknesses of these techniques make them complex [12], computationally expensive, and some are application- or context-dependent [9]. Therefore, research effort should be made to design a new low-complexity scheme, yet secure data integrity system that can detect and filter out false data injection attacks. The main contribution of this paper is the offer of a Randomized Watermarking Filtering Scheme (RWFS) for WSN, which can filter false injected data effectively and as early as possible without relying on any static node location or secure routing protocols. The proposed system aims to protect the integrity, authenticity, and confidentiality of sensory data using a lightweight watermarking technique. It will provide en-route rather than end-to-end filtering to minimize communication overhead by reducing the number of false data reports. Integrity and authenticity will be provided by embedding a watermark at random locations within the data. Embedding a watermark within multiple random locations will make it more difficult for an adversary to inject a false report. The system will also use homomorphic symmetric encryption to provide confidentiality. The main purpose of this paper is to address the following issues:  
1. Develop a novel energy-efficient scheme that aims to minimize both the packet size and number of communications between nodes. 2.