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Wireless sensor networks (WSNs) are considered to be one of the most important technologies of the 21st century. As a result, WSNs have been used in numerous applications in industry, health monitoring, environmental monitoring, and other related fields. However, the unprotected nature of WSN protocols such as the Ad-hoc On-Demand Distance Vector (AODV) Protocol makes them prone to malicious attacks. One such attack is the replay attack. A single sensor node has limited computation and communication capabilities, but processing routing information through data structures with acceptable time and space complexity can lead to secure data acquisition and sensing. Sensor nodes have limited energy resources, so this attack can have a serious impact on network functionality. In this work, Bloom filters are used to identify the legitimacy of a packet. Sensor nodes will be able to distinguish between legitimate and replayed packets along a path, thus improving a node’s decision as to whether it should transmit or discard the received packet.
A detection route refers to a route without data packets whose goal is to convince the adversary to launch an attack so the system can identify the attack behaviour and then mark the black hole location. Thus, the system can lower the trust of suspicious nodes and increment the trust of nodes in successful routing routes.
Through active detection routing, nodal trust can be quickly obtained, and it can effectively guide the data route in choosing nodes with high trust to avoid black holes.
The data routing refers to the process of nodal data routing to the sink. The routing protocol is similar to common routing protocols in WSNs ; the difference is that the route will select a node with high trust for the next hop to avoid black holes and thus improve the success ratio of reaching the sink.
The data routing is shown via the black arrow in . The routing protocol can adopt an existing routing protocol , and we take the shortest route protocol as an example
The core idea of packet routing is that when any node receives a data packet,it selects one node from the set of candidates nearer the sink whose trust is greater than the preset threshold as the next hop.
If the node cannot find any such appropriate next hop node, it will send a feedback failure to the upper node, and the upper node will re-calculate the unselected node set and select the node with the largest trust as the next hop; similarly, if it cannot find any such appropriate next hop, it sends a feedback failure to its upper node.
We consider a wireless sensor network consisting of sensor nodes that are uniformly and randomly scattered in a circular network; the network radius is , with nodal density ,
and nodes do not move after being deployed Upon detection of an event, a sensor node will generate messages,and those messages must be sent to the sink node R
Networks have been rapidly developing throughout the last few decades, varying from simple host-server structures to a thousand-node WSN architecture. In parallel, networking risks have grown as well. The more sophisticated the network, the wider the range of potential attacks that could be performed to sabotage the dedicated functionalities of that network. Replay attacks are among the most common and easily performed attacks.
This line of research studies the efficiency of Bloom filters in addressing replay attacks performed on wireless sensor networks and how they impact energy, throughput, and numbers of packets exchanged in the network. WSNs, AODV routing protocol, replay attacks, and Bloom filters are all concepts that make up the building blocks of this work, so they are discussed in detail in the following subsections to give readers a better understanding of the overall ideas and approaches
WSNs are the result of advancements in the technology fields of micro-electro-mechanical systems (MEMS) and other related areas of research, such as communication networks and embedded systems. A low-cost, less power demanding, space-efficient network structure is available for multiple uses and purposes. The field of WSNs is a fertile source of applications in industry, military, health practices, scientific research, and other sectors. Composed of inexpensive sensors triggered by the surrounding environment, WSNs can collect meaningful data, which can then be analyzed for deployment purposes.
WSNs can be deployed in terrains and disaster locations that are absolutely inaccessible. Thus, another important feature of WSNs is self-organization, which allows the nodes to form ad-hoc networks. Nodes in WSN are also capable of doing minor processing of data to minimize the size of data messages needed to be exchanged. For example, having sensor nodes close to hurricanes can improve environmental research. Monitoring a patient from a distance for any abnormal symptoms is not only relieving for the patient’s mind, but also more comfortable for the doctor in performing his/her duties more efficiently. These are some of the science and humanity serving purposes of WSNs
Replay attacks have an inevitable impact on energy storage in WSN nodes. They decrease residual energy and increase exchanged protocol and data messages. Bloom filters can salvage energy storages in different levels according to how big the WSN is and how long the path of transmission is.