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Internet of Things (IoT), as an emerging technology, describes a smart world that enables objects to interact with each other and with end-users through developed communication platforms such as the Internet. The limited communication range of things allows packets to be transmitted between objects that are not in each other's range through intermediate objects, which highlights the necessity of routing in this technology. Therefore, the Quality of Service parameters of IoT entirely depend on the cooperation of things in the routing process. Several factors such as energy depletion, conquering objects by an enemy, and resource constraints lead to selfish behavior that may cause network collapse. In this paper, a new watchdog-based identification method is presented to diagnosis selfish objects in the Internet of Things, in which the selected watchdogs are responsible for evaluating the activity of other objects. In the proposed method to achieve a uniform distribution of watchdog objects in the network, the IoT objects are partitioned into disjoint cells using the Voronoi diagram, and a watchdog is selected for each cell. In this way, while the supervision overhead is distributed, the watchdog objects can identify the selfish things through examining the remaining energy and cooperation degree of other objects. The conducted simulations in the MATLAB tool have validated the effectiveness of the proposed method in terms of detection rate, false-positive rate, throughput, end-to-end delay, and energy consumption. [ABSTRACT FROM AUTHOR]

Efficiency of position-based emergency message broadcast protocols of Vehicular Ad-hoc Networks (VANET) is affected by the position of vehicles that depends entirely on accuracy of the positioning system. In addition, GPS-based dissemination protocols encounter many challenges due to high-energy consumption, low accuracy, and the need for up-to-date maps for real-world automatic applications. In this paper, a Route Segmented Broadcast Protocol based on Radio Frequency identification technology (RSBP-RF) is proposed to eliminate GPS-based problems and increase the efficiency of emergency message broadcasting by improving the positioning accuracy. In this protocol, embedded passive RFID tags are used to segment the route and provide a position-aware technique. In RSBP-RF, each vehicle is informed of its position by crossing on the tags and immediately broadcasts a beacon to its neighbors. Moreover, the proposed protocol includes a proactive relay vehicle selection scheme that uses the received beacons to select the farthest neighboring node. The results of the conducted simulations in the NS-3 tool have validated the effectiveness of the proposed protocol in terms of end-to-end delay, delivery ratio, one-hop delay, collision ratio, relay node selection accuracy, and dissemination performance. Moreover, analysis of the time complexity and message complexity proves the lightness of the RSBP-RF protocol. [ABSTRACT FROM AUTHOR]

The positioning techniques and adjusting sensing radius of nodes have an important role in improving the network coverage of hybrid wireless sensor networks. In this paper, we propose a novel optimized bidding-based algorithm that able to identify the coverage holes after deploying the stationary sensor nodes, also adjust sensing radius of nodes effectively based on the Delaunay triangulation. In addition, if necessary, an endeavor is made to relocate a portion of mobile nodes at the appropriate positions to cover the holes and reduce the overlapping area. Furthermore, the structure of created triangles i.e. acute and obtuse, layout vertices, region coverage status in before and after the relocation are considered in moving strategy of the proposed algorithm. Extensive simulations have been performed to validate the effectiveness of the proposed algorithm in terms of the coverage ratio, number of moved nodes, number of alive nodes and average of nodes movement. [ABSTRACT FROM AUTHOR]

• Providing a lightweight RFID-based authentication approach for IoT application to address the computation and hardware limitations. • Applying nonlinear key-stream generators to eliminate the linearity property of LFSR-based generators. • Evaluating the efficiency of the proposed approach based on security analysis and statistical analysis. • Assessment of the effect of generator polynomial degree and the proposed combinational generators on the BER using the Cooja tool. • Assessment of overhead of the proposed approach in terms of computational cost, storage spaces, and communication costs. In this paper, a new lightweight RFID-based authentication approach is proposed that uses stream ciphering techniques to preserve privacy between legitimate components. The proposed approach uses various key-stream generators such as And2LFSR, Geffe, and majority-based to eliminate the inherent linearity of LFSRs and construct a non-linear Pseudo-Random Number Generators (PRNG). The informal security analysis indicates that the proposed approach provides forward security and resists eavesdropping, tag tracking, replay, cloning, and DoS attacks. Moreover, simulations conducted through Cooja tool illustrate the effect of generator polynomial degree and proposed combinational generators on BER. The BER of communications has been evaluated in different quantities of (E b / N 0), ranged between 1 and 10, using the proposed generators along with different error control techniques. In addition, evaluations indicate that the proposed approach outperforms in terms of computational cost, storage spaces, and communication cost. Furthermore, statistical analysis proves resistance of the proposed approach to guessing attacks. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: In most fault-detection algorithms in wireless sensor networks (WSNs), each sensor compares its data with the data of neighboring nodes. The majority of comparative methods will not work properly if more than half of the neighboring nodes of a sensor are faulty. Moreover, such comparative methods are unable to detect common mode failures (CMFs). Hence, having noticed the deficiencies of the existing comparative methods and as a reaction against such problems, we introduced a novel self-diagnosing approach to reduce the effect of neighboring node’s data in determining the status of nodes so that a sensor’s status will be determined independently of any comparisons. A sensor will be deemed to be fault-free if its components and the inner links between the components are flawless. In this paper, the behaviors of the components of a sensor are independently analyzed by means of the proposed model based on Petri nets and the links of the sensor’s components are investigated by means of the correlation graph. In addition, the authors extended and generalized the proposed method to all the nodes of a network and evaluated their operation. Simulation results showed that the modeling implemented by the HPSim tool can cover both permanent and transient faults accurately. Moreover, using the correlation graph and Pearson correlation coefficient helped us to gain confidence in the correctness of the inner links between the components of a sensor. Evaluation of the results indicated that the statistical results from the QI Macros tool were substantially similar to those from HPSim and Matlab tools. Furthermore, simulations results demonstrated that the detection accuracy and false alarm rate of the proposed method is acceptable even when the fault probability of each sensor is generally to be high. As a result, using these mechanisms leads to the development of the self-diagnosing capability in the sensors of a WSN. [Copyright &y& Elsevier]

A wide range of network technologies and equipment used in network infrastructure are vulnerable to Denial of Service (DoS) attacks. Therefore, the identification of these attacks is of particular importance in security systems. Most of the previously presented solutions use a single machine learning model to detect DoS attacks; but it seems that improving the detection accuracy and reliability in the intrusion detection system will be possible by using the combination of learning models. This research, is an effort to improve the accuracy of DoS attacks detection, compared to previous methods. Also, overcoming the challenge of large number of classes in intrusion detection task using ECOC based hybrid classifiers is one of the main objectives of the research. In this paper, a novel method to detect DoS attacks in computer networks is proposed. The proposed method performs the intrusion detection process in three phases named as pre-processing, feature extraction and classification. Principal Component Analysis (PCA) is used for extracting features, while a combination of Error Correcting Output Codes (ECOC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for classification. In this classification model, Particle Swarm Optimization (PSO) algorithm has been used to optimize the structure of ANFIS. The performance of the proposed method has been evaluated using the NSLKDD database. Using a 10-fold cross validation experiment, the proposed IDS showed a sensitivity of 99.82%. The results also show that the proposed method can detect the types of DoS attacks with an average accuracy of 98.9%, which shows a significant improvement compared to the previous methods. [ABSTRACT FROM AUTHOR]