## 2 Related Work  
The phantom flooding technique presented in [12] attempts to create a fake source that attracts the adversary away from the real source. It has two phases: random walk and a subsequent flooding. In the first phase, a message will be directed to a fake source within \(\mathbf{h}_{walk}\) hops from the real sender's node. The purpose of the second phase is to send the message to the sink using baseline flooding which is a kind of smart version of the flooding. Instead of transmitting every received packet, baseline flooding keeps track of all previously transmitted packets for each node. If a node received a packet that was already transmitted by itself, this node drops the packet right away. This reduces the overhead since each node in the network only transmits one copy of the packet. Another technique is an Angle Based Dynamic Routing Scheme [7], which calculates the inclination angle between the source node and the receiver as well as between the source node and final destination. Based on this calculation, a set of candidate neighbor nodes is selected randomly to forward the event. This set of candidate nodes is going to change every time an event packet is sent leading to multiple paths towards the destination. This tries to mislead the adversary away from the real source. These protocols are very effective against local adversaries, which have a partial view of the network, but they will struggle against global adversaries that have a full view of the network, as increased traffic in a neighborhood could be used to guess the source. There are many techniques reported in the literature to counter a global adversary, such as FitProbRate in [13]. In this technique, every node sends dummy messages based on the exponential distribution and if a real event is detected, the real message will be sent using the same kind of distribution, because the exponential distribution has only one parameter i.e., \(\mathbf{\lambda}=1/u\). Therefore, if \(\mathbf{\mu}\) value of the dummy distribution is similar to the \(\mathbf{\mu}\) value of the real distribution, there will be confusion for the adversary in recognizing the difference between real and fake traffic. However, the authors did not mention how the delivery ratio and delay will be controlled in their technique to meet the application's requirements. Furthermore, this protocol will increase the overhead and decrease the lifetime of the network, because it uses an A-D test every time a real event is detected. The A-D test is short for the Anderson-Darling test, which is utilized to determine if a series of data follows a specific probabilistic distribution. This approach is known as the goodness fit test. The General Fake Source (GFS) approach in [9] is another technique to counter a global adversary by simulating the real movement of the real event such as a Panda in different places in the network when the Panda has passive RFID. The basic idea is to generate the traffic of the fake sources instead of generating the fake source itself by using a token to decide which node should be the fake source. Then the fake source will send traffic as it senses a real Panda. Finally, the token will be passed in the network simulating the movement of the Panda. The problem occurs when the Panda is changing its behavior frequently; the adversary can easily detect the Panda, because fake traffic is only generated when a real event is taking place. In addition, they assume that the sink node must be in the center of the network, which is not applicable in many practical applications. Some of the applications require having the sink node on one side or at some other fixed position in the network rather than the center, which makes this technique weaker in terms of source anonymity. Another technique is the recurrent clustering mechanism in [14], where clustering is used to reduce the power consumption. Each cluster has a cluster head. Cluster heads coordinate the events in the network.