## 1 Introduction  
Wireless Sensor Networks (WSN) consist of homogeneous, small, and low-cost sensor nodes that have limitations in resources and power [1]. Usually, sensors are used to sense information such as temperature, humidity, and light, which notify the sink through other sensor nodes that act as forwarders to pass data to its final destination. WSNs can also be used for monitoring and tracking applications such as monitoring a species nearing extinction in a national park, tracking a soldier in a battlefield or surveillance of the borders of a country [2]. In WSNs, communication between nodes in transmitting and receiving consumes more power than the computation inside the sensor node itself [1]. Since WSNs have limited resources, a sensor node should only compute basic operations [1]. WSNs can be deployed in remote locations that are not reachable by wired networks such as a hostile environment or a vast forest. Thus, security of sensor networks is demanding, and it needs to be addressed very carefully [3, 4]. Security issues in WSNs are classified into content threats and context threats [5]. Content security focuses on security of the data itself, and it can be achieved by using confidentiality and encryption techniques, whereas context security such as source location privacy (SLP) focuses on concealing the location of the source node [6]. Anonymity of a node means that this node should be untraceable under any kind of statistical analysis applied by an adversary [7]. Sink Location Privacy is another context security concern. It is preventing adversaries from gaining information on the sink node location. The anonymity of the sink node is a completely different problem. The focus of this paper is only on SLP. Adversaries are restricted to two different types, local and global. Mostly, a local adversary has only a partial view of the network, and it can be countered by modifying the existing routing techniques, whereas a global adversary has a full view of the network and can analyze the entire traffic in the network [8]. Moreover, there are two types of attacks which can be employed by adversaries, active and/or passive attack. An active attack occurs when the adversary attempts to alter the network traffic by modifying the header or the content of transmitted packets or even by injecting new packets into the network to apply some attacks such as denial of service. A passive attack analyzes the network traffic without altering it by observing which sensor nodes are sending packets and which nodes are not. A passive attack is more critical and is harder to detect than an active attack, since it cannot be noticed by the system, because it is not applying any kind of modification. In WSNs, devices are often attached to the tracked asset, e.g., a Radio Frequency Identification (RFID) tag. An RFID can either employ an active or passive tag. The active tag has a battery and can send signals to sensor nodes, which can be used to simulate the movement of the asset in different locations in the network, whereas a passive tag does not have a battery and cannot send signals. We only consider passive tags in this paper, because they are cheaper and more applicable to the type of problems for WSNs identified earlier [9]. The basic idea behind the proposed techniques is as follows. When a real event is detected, it should be reported to the sink. In order to hide the real event, the network should be injected with dummy packets using as low a rate as possible to confuse the adversary. The low rate is needed to minimize the communication overhead of the network. We develop appropriate algorithms that maintain location and time privacy while minimizing dummy-packet transmissions. The algorithms developed are compared on extensive simulations to demonstrate that they maintain SLP. This is an extended research of the original work in [10]. There are many techniques used to counter a global adversary such as separate path routing, network location anonymization, network coding, and dummy data sources [11].