A Framework for Secure and Survivable Wireless Sensor Networks

Wireless sensor networks increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future. A wireless sensor network (WSN) is vulnerable to security attacks due to the insecure communication channels, limited computational and communication capabilities and unattended nature of sensor node devices, limited energy resources and memory. Security and survivability of these systems are receiving increasing attention, particularly critical infrastructure protection. So we need to design a framework that provide both security and survivability for WSNs. To meet this goals, we propose a framework for secure and survivable WSNs and we present a key management scheme as a case study to prevent the sensor networks being compromised by an adversary. This paper also considers survivability strategies for the sensor network against a variety of threats that can lead to the failure of the base station, which represents a central point of failure.key management scheme, security, survivability, WSN

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

Smartening the Environment using Wireless Sensor Networks in a Developing Country

The miniaturization process of various sensing devices has become a reality by enormous research and advancements accomplished in Micro Electro-Mechanical Systems (MEMS) and Very Large Scale Integration (VLSI) lithography. Regardless of such extensive efforts in optimizing the hardware, algorithm, and protocols for networking, there still remains a lot of scope to explore how these innovations can all be tied together to design Wireless Sensor Networks (WSN) for smartening the surrounding environment for some practical purposes. In this paper we explore the prospects of wireless sensor networks and propose a design level framework for developing a smart environment using WSNs, which could be beneficial for a developing country like Bangladesh. In connection to this, we also discuss the major aspects of wireless sensor networks.Comment: 5 page

Resilient Wireless Sensor Networks Using Topology Control: A Review

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs