Robust multiple frequency multiple power localization schemes in the presence of multiple jamming attacks

Abstract

Localization of the wireless sensor network is a vital area acquiring an impressive research concern and called upon to expand more with the rising of its applications. As localization is gaining prominence in wireless sensor network, it is vulnerable to jamming attacks. Jamming attacks disrupt communication opportunity among the sender and receiver and deeply impact the localization process, leading to a huge error of the estimated sensor node position. Therefore, detection and elimination of jamming influence are absolutely indispensable. Range-based techniques especially Received Signal Strength (RSS) is facing severe impact of these attacks. This paper proposes algorithms based on Combination Multiple Frequency Multiple Power Localization (C-MFMPL) and Step Function Multiple Frequency Multiple Power Localization (SF-MFMPL). The algorithms have been tested in the presence of multiple types of jamming attacks including capture and replay, random and constant jammers over a log normal shadow fading propagation model. In order to overcome the impact of random and constant jammers, the proposed method uses two sets of frequencies shared by the implemented anchor nodes to obtain the averaged RSS readings all over the transmitted frequencies successfully. In addition, three stages of filters have been used to cope with the replayed beacons caused by the capture and replay jammers. In this paper the localization performance of the proposed algorithms for the ideal case which is defined by without the existence of the jamming attack are compared with the case of jamming attacks. The main contribution of this paper is to achieve robust localization performance in the presence of multiple jamming attacks under log normal shadow fading environment with a different simulation conditions and scenarios.

A Survey of Security Challenges, Attacks Taxonomy and Advanced Countermeasures in the Internet of Things

“…There is still some weakness in the existing approaches such as security resilience, connection probability and scalability. [131], MAPE [126], Linear SVM [128], IoT Malware Detection [127], ELSA [130], Hybrid Spyware Detection [129], BLOWFISH, PRESENT, HEIGHT, AES, 3DES, RSA, ECC, DSA, DH [233], [40], K-Anonymity Technique [135], BUDDIES [136], LSR [146], Lightweight RSS-Based Scheme [148], Privacy-Preserving Scheme [147], Game Theory [152], [153], [234], CHA-IDS [140], TCGA [145], INTI [143], Sensor Scheduling Technique [120], Mobile Agent-Based IDS [150], Determeministic Detection [156], Jammer Localization, Detection and Mitigation [157]- [159], [182], [189], [190] and Tamper Detection [160].…”

“…In [189], [190], cancellation and the usage of different parts of the spectrum are introduced for neutralizing the jammer signals, whereas some attempt to estimate the position of the jammer for further action. Some of them have utilized autonomic computing [157]- [159] to detect jammer's location. Node Injection is another vital attack in the physical layer.…”