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Security in wireless sensor networks is an upcoming field which is quite different from traditional network security mechanisms. Many applications are dependent on the secure operation of a wireless sensor network, and have serious effects if the network is disrupted. Therefore, it is necessary to protect communication between sensor nodes. Key management plays an essential role in achieving security in wireless sensor networks. To achieve security, various key predistribution schemes have been proposed without deployment knowledge. Deployment knowledge can benefit the key predistribution scheme, as the nodes that are likely to be neighbors of each sensor node are assumed, and hence each node does not need to waste its memory to store unnecessary secret information. However, the existing key predistribution schemes require more memory and larger transmission range to achieve the desired connectivity. In order to enhance secure communication among sensor nodes, grid and hexagon deployment models are used. In both the deployment models, the sensor field is divided into equal grids. The keys are distributed from a large key pool randomly and pairwise keys are generated for each pair of sensor nodes. Once the pairwise keys are generated among neighboring nodes, they establish a secure connection and transmit data. We propose a new scheme based on cell splitting concept to improve the security level, in which a hexagon is subdivided in to smaller groups. The performance is evaluated in terms of connectivity and resilience against node capture. The analysis show that the performance is better using cell splitting concept compared to normal hexagon based scheme.

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A jamming attack is a special case of a Denial of Service (DoS) attack that completely blocks the data transmission in Wireless Sensor Networks (WSNs). When sensor nodes are distributed in the field, numerous attacks, such as collision, black hole, selective forwarding, jamming, etc., caused by the presence of malicious nodes have the potential to cause network damage. Jamming is a highly risky attack that completely blocks data transmission within the wireless network. The existing technique for detecting jamming attacks are based on predetermined hopping-sequence, cryptographic, or random frequency hopping techniques. However, these mechanisms are more complex and frequently have energy constraints and high overhead. A novel jamming detection method based on a statistical approach that provides high network performance measures is proposed. It is a technique that uses energy-based clustering with a Received Signal Strength Indicator (RSSI). The selection of thresholds used for the detection of jamming is analyzed. The proposed approach employs three detection performance metrics for investigating the jamming attack, namely, Packet to Delivery Ratio (PDR), ENERGY, and RSSI. The jamming node is identified using the Optimal Decision Rule (ODR), which is determined by the hypothesis rule. If the hypothesis is not satisfied, then jamming exists; otherwise, there is no jamming. The novel technique is implemented using a Network Simulator, and various performance metrics such as PDR, Energy consumption, Network throughput, Routing overhead, network, and node lifetime are evaluated to conclude that the statistical approach outperforms the timestamp and IEWMA approaches.

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