## 1 Introduction  
Internet of Things (IoT) research has been considered as a paramount domain while enhancing current technologies such as wireless sensor networks (WSNs) [1]. To assist individuals in finding information in various fields, IoT deployment intends to collect data via a wireless connection. IoT is easily deployed in locations with widespread internet access. However, in wilderness areas such as wildlife sanctuaries, military battlefields, polar regions of the earth, etc., internet connectivity is inadequate or unreliable. In such cases, WSNs play a key role in mitigating this issue. The sensor nodes are primarily used to monitor and track priceless assets, such as reporting in-the-moment military data from a battleground, tracking the whereabouts of threatened species in their habitats [2, 3, 4, 5, 6, 7], etc. In particular, the sensors form a mesh network and send the collected data wirelessly to single or multiple central controllers known as base stations (BSs). It is observed that establishing a direct communication link between IoT nodes and the sink node leads to a noteworthy decrease in the remaining energy levels of the individual nodes [8]. Therefore, within the context of IoT applications, ensuring an adequate battery charge within the IoT nodes holds significant importance. Various research initiatives are dedicated to addressing challenges associated with energy consumption and the prolonged lifetime of networks. [PERSON] et al. in [9] endeavored to address concerns regarding the optimal placement of RF-energy transmitters by introducing a network-aware scheme for positioning these transmitters. This approach becomes pivotal, as radio frequency (RF) stands out as one of the primary ambient energy sources viable for energy harvesting. [PERSON] et al. conducted an in-depth study on the RF-powered AB-assisted hybrid underlay cognitive radio network (ABHU-CRN), with the aim of enhancing the long-term secondary throughput in [10]. This research endeavor was undertaken to tackle the critical challenges posed by energy and spectrum constraints within wireless sensor networks. Numerous approaches have been introduced by various researchers to enhance the operational lifespan of networks. These methods encompass optimal power schemes, as outlined in Reference [11], which facilitate a systematic utilization of the nodes' remaining energy. Additionally, the clustering of IoT nodes, as discussed in Reference [12], has been employed. Furthermore, intelligent energy utilization approaches, such as innovative routing techniques and the application of wake-up radio protocols detailed in References [13; 14], have also contributed to enhancing network lifetime. Since these WSNs are deployed in internet-devoid zones, the BS is supposed to be connected to an IoT gateway that is situated in common places where an internet facility is available. Data collected from the WSN can then be forwarded to the operator, via IoT for live tracking and monitoring purposes. However, alongside network lifetime, there are other undesirable issues associated with these networks. The nature of wireless communication channels makes security and privacy crucial considerations for these networks. The attacker may employ _active attacks_ such as packet dropping or _passive attacks_ such as eavesdropping on the signals. Several solutions have been proposed in the literature by various researchers to mitigate such attacks [6; 15; 16; 17; 18; 19; 20; 21; 22]. Source location privacy (SLP) protection techniques are the name given to these solutions. One such domain we explore is contextual privacy [7]--it deals with hiding contextual information such as packet rate, routing paths, identities of sensor nodes, etc. Particularly, this paper aims at protecting the source location information from the backtracking attacker. We consider wildlife applications where sensor nodes are deployed to keep track of endangered animals such as rhnos, pandas, tigers, etc.