Petri nets‐based model for the analysis of NORIA protocol

Abstract

Network rOle-based Routing Intelligent Algorithm is a novel routing algorithm for wireless sensor networks, which combine various effective techniques in order to reduce energy consumption and improve data routes. This algorithm uses role assignment for distributing tasks over the network nodes and fuzzy logic for making decisions. There is a clear need for the use of formal methods to validate the correctness of the protocols as well as performance and functionality prior to the deployment of such algorithms in a real environment. This paper presents a formal and rigorous study of Network rOle-based Routing Intelligent Algorithm. Prioritised-timed coloured petri nets (PTCPNs) have been chosen as an appropriate modelling language. In this way, PTCPNs have been used to describe complete and unambiguous specifications of system behaviour, whereas CPNTools is used to evaluate the correctness of the protocol using state space exploration and for performance evaluation using simulation. ANALYSIS OF NORIA PROTOCOL 4705 routing intelligent algorithm called Network rOle-based Routing Intelligent Algorithm (NORIA)[3] will be analysed in this paper. NORIA improves data routing in networks oriented mainly to monitoring applications, that is, those in which the information is gathered individually in each node and it is forwarded bottom-up to a special sink called here base station, which is located at the centre. Thus, the network is organised forming a tree. Furthermore, NORIA uses fuzzy logic to make routing decisions, and each node has assigned a role that will determine its functionality.Moreover, we advocate for the use of prioritised-timed coloured Petri nets (PTCPNs) [4] for several reasons. First, PTCPNs are based on rigorous mathematical definitions so that one may be capable to obtain complete and unambiguous specifications of the system behaviour. Besides, PTCPNs have a graphical notation, allowing users to track easily how the system evolves, and provide several analysis methods, including simulation, state space analysis and invariant analysis. In the literature, there are some tools for constructing and evaluating PTCPN models, but we have opted for CPNTools [5] because it is considered the de facto tool for editing, simulating and analysing PTCPNs and supports hierarchical nets, priorities and time. In addition, the toolbox provides also the necessary primitives to make your own queries (written as ML functions) to study interesting properties of the system state space. Finally, the tool monitor facilities permit us to extract relevant information from the simulations in order to evaluate system performance. Figure 3. CPN model for a node (part II).Here, the transitions Receive IPM and Receive IPM_out simulate the reception of an IPM, but the main difference between both is that the first one can be fired during that 51 units of waiting for packets and the packets information is used for the parent selection, whereas the second one (Receive IPM_out) is used to receive IPMs out of the duration of the timer. Ne...

Misbehavior analysis of IEEE 802.11 MAC layer in mobile ad hoc network using stochastic reward nets

“…10,24 CW Manipulation: Here, a node will always select a backoff value from a different distribution with a smaller average backoff value than the distribution specified by the DCF, eg, by selecting cw2 from [0, γCW] (0 ≤ γ < 1) instead of [0, CW] or not doubling its CW when collision happens. While well-behaved nodes strictly obey the protocol operations, the misbehaving nodes may deviate from the directed operations to either cause unfairness or disrupt the network usual services.…”

“…We symbolized this probability with δ, which is expressed using Equation 10. T NE-MRL points to the probability that a node would not succeed to transmit its data rather reaches to its MRL number of try.…”

“…Contention Window, NAV value, DIFS interval, and Data transmission speed are the main parameters of IEEE 802.11 DCF, which may be manipulated by a misbehavior node. 10,24 CW Manipulation: Here, a node will always select a backoff value from a different distribution with a smaller average backoff value than the distribution specified by the DCF, eg, by selecting cw2 from [0, γCW] (0 ≤ γ < 1) instead of [0, CW] or not doubling its CW when collision happens.…”

“…[1][2][3] The principal usage of MANET usually needs secure and reliable operations. 9,10 Additionally, investigation of MANET's high-level specifications is usually difficult using these network simulators. Malicious nodes and their misbehavior's operations are one of the most important reasons that make the MANET vulnerable.…”

“…Considerable time and resources consumed for running a large and almost complicated scale of scenarios are another disadvantage of using these simulators. 9,10 Additionally, investigation of MANET's high-level specifications is usually difficult using these network simulators. This is because the network simulators implement the network at a low level of abstraction and higher-level specifications cannot be supported well.…”