Game theory based Ad-hoc On Demand Distance Vector Routing Protocol to Extend the Wireless Sensor Networks Life Time

This paper proposes a solution to increase the energy life time of wireless sensor networks (WSNs) via a concept of game theory enabled ad-hoc on demand distance vector (AODV) routing algorithm. Game theory is an optimal promising candidate for decision making in a wireless networking scenario to find the optimal path for data packets transfer between source node and destination node, where combination with the AODV routing algorithm, a procedure of game theory enabled AODV (GTEAODV) is developed and proposed in this research paper. The developed and proposed methodology is validated through simulation in NS2 environment and the results show an improvement in energy life time of the order of 30-35% in comparison to the existing routing methodology which uses co-operative routing techniques among the nodes in WSN. Further, the throughput of game theory enabled adhoc on demand routing is also highly improved in comparison to existing traditional approaches though obtained results. Though, game theory approach is an existing approach concatenation of it with AODV can provide increased network performance which is significant as portrayed in research results shown in the paper. Hence, by virtue of providing enhanced energy life time and data security through the nature of the algorithm, the proposed GTEAODV algorithm can be employed in defence applications for secure data transmission and reception for forthcoming deployment of 5G systems which are blossoming in world wide scenario

Secure Routing in Wireless Mesh Networks

Wireless mesh networks (WMNs) have emerged as a promising concept to meet the challenges in next-generation networks such as providing flexible, adaptive, and reconfigurable architecture while offering cost-effective solutions to the service providers. Unlike traditional Wi-Fi networks, with each access point (AP) connected to the wired network, in WMNs only a subset of the APs are required to be connected to the wired network. The APs that are connected to the wired network are called the Internet gateways (IGWs), while the APs that do not have wired connections are called the mesh routers (MRs). The MRs are connected to the IGWs using multi-hop communication. The IGWs provide access to conventional clients and interconnect ad hoc, sensor, cellular, and other networks to the Internet. However, most of the existing routing protocols for WMNs are extensions of protocols originally designed for mobile ad hoc networks (MANETs) and thus they perform sub-optimally. Moreover, most routing protocols for WMNs are designed without security issues in mind, where the nodes are all assumed to be honest. In practical deployment scenarios, this assumption does not hold. This chapter provides a comprehensive overview of security issues in WMNs and then particularly focuses on secure routing in these networks. First, it identifies security vulnerabilities in the medium access control (MAC) and the network layers. Various possibilities of compromising data confidentiality, data integrity, replay attacks and offline cryptanalysis are also discussed. Then various types of attacks in the MAC and the network layers are discussed. After enumerating the various types of attacks on the MAC and the network layer, the chapter briefly discusses on some of the preventive mechanisms for these attacks.Comment: 44 pages, 17 figures, 5 table

Energy Aware Algorithms for managing Wireless Sensor Networks

While the majority of the current Wireless Sensor Networks (WSNs) research has prioritized either the coverage of the monitored area or the energy efficiency of the network, it is clear that their relationship must be further studied in order to find optimal solutions that balance the two factors. Higher degrees of redundancy can be attained by increasing the number of active sensors monitoring a given area which results in better performance. However, this in turn increases the energy being consumed. In our research, we focus on attaining a solution that considers several optimization parameters such as the percentage of coverage, quality of coverage and energy consumption. The problem is modeled using a bipartite graph and employs an evolutionary algorithm to handle the activation and deactivation of the sensors. An accelerated version of the algorithm is also presented; this algorithm attempts to cleverly mutate the string being considered after analyzing the desired output conditions and performs a calculated crossover depending on the fitness of the parent strings. This results in a quicker convergence and a considerable reduction in the search time for attaining the desired solutions. Proficient cluster formation in wireless sensor networks reduces the total energy consumed by the network and prolongs the life of the network. There are various clustering approaches proposed, depending on the application and the objective to be attained. There are situations in which sensors are randomly dispersed over the area to be monitored. In our research, we also propose a solution for such scenarios using heterogeneous networks where a network has to self-organize itself depending on the physical allocations of sensors, cluster heads etc. The problem is modeled using a multi-stage graph and employs combinatorial algorithms to determine which cluster head a particular sensor would report to and which sink node a cluster head would report to. The solution proposed provides flexibility so that it can be applied to any network irrespective of density of resources deployed in the network. Finally we try to analyze how the modification of the sequence of execution of the two methods modifies the results. We also attempt to diagnose the reasons responsible for it and conclude by highlighting the advantages of each of the sequence

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

© 2018 Elsevier Inc. An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption