scholarly journals Rank-Based Report Filtering Scheme (RRFS) for Verifying Phoney Reports in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Gayathri Santhosh ◽  
Yogesh Palanichamy

Wireless sensor networks (WSNs) are open to false data injection attack when they are deployed in hostile scenarios. Attackers can easily deceive the sink by compromising sensing nodes or by injecting phoney data into the network. Such attacks can deplete the energy resources of the network by providing wrong information which in turn can affect the proper network functioning or sometimes can shut the network from further functioning. The existing schemes that deal with this problem focus on only a few aspects of the false data injection attack. To resolve this problem, we propose a Rank-based Report Filtering Scheme (RRFS), a holistic and group verification scheme for the identification of compromised nodes and the filtering of false data injected into the network. The proposed scheme verifies report among clusters, en-routers, and sink. Hence, the RRFS, a holistic scheme that is composed of three-tier verifications, successfully rejects the false data before the attackers falsify the whole environment, and this makes the system unique. Reliability Index (RI) is calculated by the nodes for fellow cluster members, and the cluster head (CH) provides the score for a node based on its RI. This, in turn, strengthens the scheme by assisting the en-routers to detect the compromised nodes. The RRFS scheme has been verified and validated by extensive simulation and meticulous performance evaluation of filtering efficiency and energy consumption against various schemes. The scheme gives high filtering efficiency against the multiple compromised nodes and also improves the network’s lifespan. The sustainability of RRFS against numerous attacks that are launched in the sensor environment is thoroughly investigated.

2020 ◽  
Vol 10 (21) ◽  
pp. 7886
Author(s):  
Atefeh Rahiminasab ◽  
Peyman Tirandazi ◽  
M. J. Ebadi ◽  
Ali Ahmadian ◽  
Mehdi Salimi

Wireless sensor networks (WSNs) include several sensor nodes that have limited capabilities. The most critical restriction in WSNs is energy resources. Moreover, since each sensor node’s energy resources cannot be recharged or replaced, it is inevitable to propose various methods for managing the energy resources. Furthermore, this procedure increases the network lifetime. In wireless sensor networks, the cluster head has a significant impact on system global scalability, energy efficiency, and lifetime. Furthermore, the cluster head is most important in combining, aggregating, and transferring data that are received from other cluster nodes. One of the substantial challenges in a cluster-based network is to choose a suitable cluster head. In this paper, to select an appropriate cluster head, we first model this problem by using multi-factor decision-making according to the four factors, including energy, mobility, distance to centre, and the length of data queues. Then, we use the Cluster Splitting Process (CSP) algorithm and the Analytical Hierarchy Process (AHP) method in order to provide a new method to solve this problem. These four factors are examined in our proposed approach, and our method is compared with the Base station Controlled Dynamic Clustering Protocol (BCDCP) algorithm. The simulation results show the proposed method in improving the network lifetime has better performance than the base station controlled dynamic clustering protocol algorithm. In our proposed method, the energy reduction is almost 5% more than the BCDCP method, and the packet loss rate in our proposed method is almost 25% lower than in the BCDCP method.


Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.


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