WSNs (Wireless Sensor Networks) have recently gained popularity. WSNs are typically deployed in insecure, unstructured areas where their source location reveals critical information about targets. Sensor deployment in WSNs has been seen in a variety of applications that oversee events and send information to base stations. Optimal route selection and source location privacy are critical issues in WSNs. If an intruder determines the source node by studying traffic mode, an attack could be conducted on a target with ease. Previous methods were based on MSROs (Mobile Sink-based Route Optimizations) and Cloud-Based WSN Protection Schemes. This research work chooses the optimum multi-sink node based on the BFAs (Bacteria Foraging Algorithms). But, it does not yield optimal paths to balance the PDRs (Packet Delivery Ratios) and energy dissipation. To seek a solution to this issue, this study proposes ERR-CSLPPs (Efficient and Reliable Routing with Cloud-based Source-Location Privacy Protections) using AAFBOA (Adaptive Adjustment Factor based Butterfly Optimization Algorithm), allowing the approach to choose the mobile sink node. Depending on the ETCs (Expected Transmission Counts), residual energy, and hop count, the optimal paths are chosen with the help of TOPSISs for efficient transmissions. The actual packets are sent over the preferred path. Next, the cloud-like false hotspot is formed to include counterfeit packets into the WSN to confuse the intruder and yield an elaborate privacy location. Counterfeit packets are included along the delivery path of the actual data packet to extend the time needed for tracing the traffic flow. The experiments reveal that the proposed system yields improved performance when matched with the earlier system in terms of overall energy dissipation, node utilization ratio, transmission delay, security, and network lifetimes.