Development of Energy and Delay Efficient Protocols for WSAN

Wireless sensor-actor network (WSAN) is a collection of resource conservative sensors and few resource-rich actors. It is widely used in various applications such as environmental monitoring, battlefield surveillance, industrial process control, and home applications. In these real-time applications, data should be delivered with minimum delay and energy. In this thesis, delay and energy efficient protocols are designed to achieve these objectives. The first contribution proposes a delay and energy aware coordination protocol (DEACP) to improve the network performance. It consists of two-level hierarchical K-hop clustering and backup cluster head (BCH) selection mechanism to provide coordination among sensors and actors. Further, a priority based event forwarding mechanism has also been proposed to forward the maximum number of packets within the bounded delay. The simulation results demonstrate the effectiveness of DEACP over existing protocols. In the second work, an interference aware multi-channel MAC protocol (IAMMAC) has been suggested to assign channels for the communication among nodes in the DEACP. An actor assigns the static channels to all of its cluster members for sensor-sensor and sensor-actor coordination. Subsequently, a throughput based dynamic channel selection mechanism has been developed for actor-actor coordination. It is inferred from the simulation results that the proposed IAMMAC protocol outperforms its competitive protocols. Even though its performance is superior, it is susceptible to be attacked because it uses a single static channel between two sensors in the entire communication. To overcome this problem, a lightweight dynamic multi-channel MAC protocol (DM-MAC) has been designed for sensor sensor coordination. Each sensor dynamically selects a channel which provides maximum packet reception ratio among the available hannels with the destination. The comparative analysis shows that DM-MAC protocol performs better than the existing MAC protocols in terms of different performance parameters. WSAN is designed to operate in remote and hostile environments and hence, sensors and actors are vulnerable to various attacks. The fourth contribution proposes a secure coordination mechanism (SCM) to handle the data forwarding attacks in DEACP. In the SCM, each sensor computes the trust level of its neighboring sensors based on the experience, recommendation, and knowledge. The actor analyzes the trust values of all its cluster members to identify the malicious node. Secure hash algorithm-3 is used to compute the message authentication code for the data. The sensor selects a neighbor sensor which has the highest trust value among its 1-hop sensors to transfer data to the actor. The SCM approach outperforms the existing security mechanisms

Fast brain tumour segmentation using optimized U-Net and adaptive thresholding

Brain tumour segmentation evolved as the dominant task in brain image processing. Most of the contemporary research proposals devise deep neural networks and sparse representation to address this issue. These methods inherently suffer from high computational cost and additional memory requirements. Thus, optimization of the computational cost became a challenging task for the contemporary research. This paper discusses an optimized U-Net model with post-processing for fast brain tumour segmentation. The proposed model includes two phases: training and testing. Training phase computes weights for optimized U-Net and an adaptive threshold value. In the testing phase, a trained U-Net model predicts a rough tumour segment. Adaptive thresholding grabs the final tumour with improved segmentation results. We have considered a brain tumour dataset of 3064 images with three types of brain tumours for evaluation. Our proposed model exhibits superior results than the existing models in terms of recall and dice similarity metrics. It exhibits competitive performance in accuracy and precision. Moreover, the proposed model outperforms its competitive models in training time

Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model

Wireless sensor networks (WSNs) consist of a large number of sensor nodes that are distributed to capture the information about an area of interest. In WSN, many of the secure data aggregation works are conducted without addressing the authentication process. It is challenging to implement authentication while preserving the energy consumption in the network. The previous research that focus on these issues have several limitations, such as sharing the security key and the key length with a base station node, and not much attention is given to enhance the authentication of the Medium Access Control (MAC) server. This makes the data aggregation network are exposed to malicious activities. This paper presents a new protocol to address the security and energy issue in Wireless Sensor Network (WSN). This newly developed protocol is named Secure and Energy-Efficient Data Aggregation (SEEDA), which is the extension of SDAACA protocol. The proposed protocol aims to enhance authentication by generating a random value and random timestamp with a secret key. The base station node will verify the fake aggregated data when the packets are received using the generated key earlier. Furthermore, the attacks are detected and prevented by utilizing secure node authentication, data fragmentation algorithms, fully homomorphic encryption, and access control model. The secure node authentication algorithm prevents attacks from accessing the network. To avoid network delays, the base station node utilizes the distance information between the participating nodes. To ensure the reliability of our proposed method, we simulate two well-known attacks, called Sybil and sinkhole attacks. Several experimental scenarios are conducted to observe their effect. Evaluation metrics such as malicious activity detection rate, energy consumption, end-to-end delay, and resilience time are measured. The performance of the proposed protocol is compared with SDA, SDAT, SDALFA, EESSDA, SDAACA, and EESDA, which is a widely used protocol in the area of secure data aggregation. The simulation results show that the proposed SEEDA method outperforms the existing scheme with 98.84% malicious nodes detection rate, 3.04 joules for energy consumption, the maximum delay of 0.038 seconds, and the resilient time 0.054, 0.075 seconds when 8%,16% of malicious nodes affecting the network. © 2013 IEEE