Residual Energy Based Cluster-head Selection in WSNs for IoT Application

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select action Trust or Mistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select action Trust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose action Trust as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism

Trust-based energy efficient routing protocol for wireless sensor networks

Wireless Sensor Networks (WSNs) consist of a number of distributed sensor nodes that are connected within a specified area. Generally, WSN is used for monitoring purposes and can be applied in many fields including health, environmental and habitat monitoring, weather forecasting, home automation, and in the military. Similar, to traditional wired networks, WSNs require security measures to ensure a trustworthy environment for communication. However, due to deployment scenarios nodes are exposed to physical capture and inclusion of malicious node led to internal network attacks hence providing the reliable delivery of data and trustworthy communication environment is a real challenge. Also, malicious nodes intentionally dropping data packets, spreading false reporting, and degrading the network performance. Trust based security solutions are regarded as a significant measure to improve the sensor network security, integrity, and identification of malicious nodes. Another extremely important issue for WSNs is energy conversation and efficiency, as energy sources and battery capacity are often limited, meaning that the implementation of efficient, reliable data delivery is an equally important consideration that is made more challenging due to the unpredictable behaviour of sensor nodes. Thus, this research aims to develop a trust and energy efficient routing protocol that ensures a trustworthy environment for communication and reliable delivery of data. Firstly, a Belief based Trust Evaluation Scheme (BTES) is proposed that identifies malicious nodes and maintains a trustworthy environment among sensor nodes while reducing the impact of false reporting. Secondly, a State based Energy Calculation Scheme (SECS) is proposed which periodically evaluates node energy levels, leading to increased network lifetime. Finally, as an integrated outcome of these two schemes, a Trust and Energy Efficient Path Selection (TEEPS) protocol has been proposed. The proposed protocol is benchmarked with A Trust-based Neighbour selection system using activation function (AF-TNS), and with A Novel Trust of dynamic optimization (Trust-Doe). The experimental results show that the proposed protocol performs better as compared to existing schemes in terms of throughput (by 40.14%), packet delivery ratio (by 28.91%), and end-to-end delay (by 41.86%). In conclusion, the proposed routing protocol able to identify malicious nodes provides a trustworthy environment and improves network energy efficiency and lifetime

Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, And Resilience

Hybrid sensor networks (HSN) consist of both static and mobile sensors deployed to fulfill a common monitoring task. The hybrid structure generalizes the network’s design problem and offers a rich set of possibilities for a host of environmental monitoring and anomaly detection applications. HSN also raise a new set of research questions. Their deployment and optimization provide unique opportunities to improve the network’s monitoring performance and resilience. This thesis addresses three challenges associated with HSN related to the collaboration, optimization, and resilience aspects of the network. Broadly speaking, these challenges revolve around the following questions: (1) how to collaboratively allocate the static sensors and devise the path planning of the mobile sensors to improve the monitoring performance? (2) how to select and optimize the sensor portfolio (the mix of each type of sensors) under given cost constraints? And (3) how to embed resilience in a HSN to sustain the monitoring performance in the face of sensor failures and disruptions? In part I, collaboration, this thesis develops a novel deployment strategy for HSN. The strategy solves the static sensor allocation problem, the mobile sensor path planning problem, and most importantly, the collaboration between these two types of sensors. Previous research in this area has addressed these problems separately in simplified environments. In this thesis, a collaborative deployment strategy of HSN is developed to improve the ultimate monitoring performance in complex environments with obstacles and non-uniform risk distribution. In part II, optimization, this thesis addresses the HSN sensor portfolio selection problem. It investigates the tradeoff between the static and mobile sensors to achieve the optimal monitoring performance under different cost constraints. Previous research in this area has studied the optimization problem for networks with a single type of sensor. In this thesis, a general optimization problem is formulated for HSN with static and mobile sensors and solved to identify the optimal portfolio mix and its main drivers. In part III, resilience, this thesis identifies monitoring resilience as a key feature enabled by HSN. This part focuses on the performance degradation of HSN in the presence of sensor failures and disruptions, and it identifies the means to embed resilience in a HSN to mitigate this performance degradation. Monitoring resilience is achieved by accounting for potential sensor failures in the deployment strategy of both static and mobile sensors through a novel, carefully designed probability sum technique. Previous research in this area has examined the reliability problem from a coverage point of view. This thesis extends the scope of investigation of HSN from reliability to resilience, and it shifts the focus from coverage considerations to the actual monitoring performance (e.g., detection time lag) and its resilience in the face of disruptions. To demonstrate and validate this novel perspective on HSN and the associated technical developments, this thesis focused on two examples of fire detection in a multi-room apartment using temperature sensors and CO leak detection in a 3D space station module with ventilation system. Three metrics are adopted as the ultimate monitoring performance, namely the detection time lag, the anomaly source localization uncertainty, and the state estimation error. A simulation environment based on the advection-conduction heat propagation model is developed for the computational experiments. The results (1) demonstrate that the optimal collaborative deployment strategy allocates the static sensors at high-risk locations and directs the mobile sensors to patrol the rest of the low-risk areas; (2) identify a set of conditions under which HSN significantly outperform purely static and purely mobile sensor networks across the three performance metrics here considered; and (3) establish that while sensor failures can considerably degrade the monitoring performance of traditional static sensor networks, the resilient deployment of HSN drastically reduces the performance degradation.Ph.D