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Data security is becoming increasingly important with the growing popularity of smart management systems combining wireless sensor networks (WSNs) and intelligent systems in industrial, agricultural, and construction fields. Traditional security methods for WSN have focused on data integrity and the identification of outliers in sensor data and are therefore vulnerable to attacks such as false positive attacks (FPAs) and false negative attack (FNAs) using massively compromised nodes. these attacks significantly compromise the sensor nodes because the correlation between node verification behaviors is not considered in the communication process until the intercepted sensor data are used as input into the system. This study introduces an FPA and FNA detection method using a spatiotemporal historical data-based knowledge base. The main contribution of the present study is the recognition of abnormal correlations using behavior monitoring through the discrete event system specification model. By recognizing abnormal correlations, the proposed method prevents the inflow of false data generated as a result of widespread damage. Furthermore, a new strategy is proposed to maximize the lifetime of the network by blocking any compromised nodes. The proposed spatiotemporal data-based detection approach is applicable to a wide variety of fields owing to its use of a shared security model. The proposed method was shown to reduce the energy consumption by 46.737% and 41.927% in comparison to statistical en-route filtering and cluster-based false-data filtering, respectively.

In wireless sensor networks, sensors are extremely vulnerable to false positive and false negative attacks due to their stringent energy and computational constraints. Several en-route filtering schemes mainly focus on saving energy through early detection of false data within a short distance against these attacks; however, they cannot immediately block the false data injected by compromised nodes. A security scheme uses context-aware architecture for a probabilistic voting–based filtering scheme to detect the compromised nodes and block the injection of false data, unlike security protocols. Although these schemes effectively obstruct the false data forwarding, they cannot make any detour around the compromised node to avoid it during data forwarding. In this article, we propose a discrete event simulation–based energy efficient path determination scheme that takes a detour around the compromised node against the attacks. Our proposed scheme extracts candidate paths considering the network status and selects a path with the highest energy efficiency from among the candidates using discrete event simulation. Simulation results indicate that the proposed scheme provides energy savings of up to 12% while maintaining the security strength against the two attacks compared to the existing schemes.

In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.

Generally, simulation models are constructed to replicate and predict the behavior of real systems that currently exist or are expected to exist in the future. Once a simulation model is implemented, the model can be connected to a real system for which the model has been built through sensors or networks so that important activities in the real system can be monitored indirectly through the model. This article proposes a modeling formalism BM-DEVS (Behavior Monitor-DEVS) that defines simulation models capable of monitoring the desired behavior patterns within the models so that the target system’s behavior can be monitored indirectly. In BM-DEVS, an extension of classic Discrete Event System Specification (DEVS), the behavior to be monitored is expressed as a set of temporal logic (TL) production rules within a multi-component model that consists of multiple component models to be monitored. An inference engine module for reasoning with the TL rules is designed based on the abstract simulator that carries out instructions in the BM-DEVS models to perform the simulation process. The major application of BM-DEVS is in the design and implementation of the context-aware architecture needed for various intelligent systems as a core constituent. Essentially all systems where some form of behavior monitoring is required are candidate applications of BM-DEVS. This research is motivated by the view that there exists symmetry between the real-world and the cyber world, in that the problems in both environments should be expressed with the same basic constituents of time and space; this naturally leads to adopting spatiotemporal variables composed of simulation models and developing a problem solver that exploits these variables.

In Wireless sensor networks, energy efficiency can be achieved by adaptive choice of the data forwarding path to balance the energy dissipation in the network. This adaptive path selection is done through a fuzzy rule-based method given the input parameters. Due to uncertainty in reasoning and inferencing process and imprecision in the data, the fuzzy-based system becomes an ideal choice for the selection of the paths. In fuzzy systems, the membership functions need to be optimized to make the best use of the fuzzy inferencing and improve the performance of the fuzzy system. Genetic algorithm-based fuzzy membership function optimization technique selects the optimal solution in a feasible time and saves from the hassle of manual intervention. Manual optimization efforts are unfeasible for common applications and take unlimited time and human expertise to optimize functions in an exhaustive search field. This technique assesses the fitness of the membership functions through simulation outcomes and optimizes them through genetic algorithm based evaluation process. The proposed scheme consists of three modules; The first module simulates the membership function in the given network model, the second module analyzes the performance efficiency of the membership functions through simulation, and the last module constructs the subsequent membership-function populations using GA techniques. The proposed method automatically optimizes the membership functions in the fuzzy system with little human intervention, requires minimal human expertise and saves ample time in the optimization process.

In applications of wireless sensor networks, there are many security issues. Attackers can create false reports and transmit the reports to the networks. These false reports can lead not only false alarms, but also the depletion of limited energy resources. In order to filter out such false reports during the forwarding process, Ye et al. proposed the statistical en-route filtering (SEF). Several research efforts to enhance the efficiency of SEF have been made. Especially, the path selection method proposed by Sun et al. can improve the detection power of SEF by considering the information on the filtering keys of and distances of upstream paths. However, such selection mechanism could lead to favored paths in heavy traffic, which would result in unbalanced energy consumption. In this paper, we propose a path renewal method to provide load balancing for sensor networks in terms of energy consumption. In our method, a node renews its upstream path to save energy resources if the remaining energy of and the communication traffic of the node exceed some threshold values. We show the effectiveness of the proposed method in terms of balanced energy consumption and filtering power by providing simulation results.

Wireless sensor networks are supplied with limited energy resources and are usually installed in unattended and unfriendly environments. These networks are also highly exposed to security attacks aimed at draining the energy of the network to render it unresponsive. Adversaries launch counterfeit report injection attacks and false vote injection attacks through compromised sensor nodes. Several filtering solutions have been suggested for detecting and filtering false reports during the multi-hop forwarding process. However, almost all such schemes presuppose a conventional underlying protocol for data routing that do not consider the attack status or energy dissipation on the route. Each design provides approximately the equivalent resilience in terms of protection against compromised node. However, the energy consumption characteristics of each design differ. We propose a fuzzy adaptive path selection to save energy and avoid the emergence of favored paths. Fresh authentication keys are generated periodically, and these are shared with the filtering nodes to restrict compromised intermediate filtering nodes from the verification process. The scheme helps delay the emergence of hotspot problems near the base station and exhibits improved energy conserving behavior in wireless sensor networks. The proposed scheme provides an extended network lifetime and better false data filtering capacity.

Authentication is an important security mechanism for detecting forged messages in a sensor network. Each cluster head (CH) in dynamic key distribution schemes forwards a key dissemination message that contains encrypted authentication keys within its cluster to next-hop nodes for the purpose of authentication. The forwarding path of the key dissemination message strongly affects the number of nodes to which the authentication keys in the message are actually distributed. We propose a routing method for the key dissemination messages to increase the number of nodes that obtain the authentication keys. In the proposed method, each node selects next-hop nodes to which the key dissemination message will be forwarded based on secret key indexes, the distance to the sink node, and the energy consumption of its neighbor nodes. The experimental results show that the proposed method can increase by 50–70% the number of nodes to which authentication keys in each cluster are distributed compared to geographic and energy-aware routing (GEAR). In addition, the proposed method can detect false reports earlier by using the distributed authentication keys, and it consumes less energy than GEAR when the false traffic ratio (FTR) is ≥10%.

Eccrine poromas are benign, slow-growing, solitary tumors originating from the intraepidermal portion of eccrine sweat ducts. Approximately 65% of these tumors occur on the soles of the feet, while 10% occur on the hands where a high concentration of eccrine sweat glands exists. Less frequently it occurs in other sites such as neck, chest, forehead, nose, and scalp with sporadic occurrences. A 43-year-old Korean female presented with a mass on her right auricle, which had been present for 5 years. The mass increased gradually in size with pain, oozing, and bleeding. A biopsy of the mass revealed monomorphic basaloid cells, which may extend into the underlying dermis, in a richly vascularized stroma, with a variable number of cystic or ductal structures. The patient was diagnosed as having eccrine poroma. In this case, the eccrine poroma showed unusual clinical presentation.