Enhancing Energy Efficiency of Wireless Sensor Network through the Design of Energy Efficient Routing Protocol

Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled “Position Responsive Routing Protocol” (PRRP). PRRP is designed to minimize energy consumed in each node by (1) reducing the amount of time in which a sensor node is in an idle listening state and (2) reducing the average communication distance over the network. The performance of the proposed PRRP was critically evaluated in the context of network lifetime, throughput, and energy consumption of the network per individual basis and per data packet basis. The research results were analyzed and benchmarked against the well-known LEACH and CELRP protocols. The outcomes show a significant improvement in the WSN in terms of energy efficiency and the overall performance of WSN

Swarm formation morphing for congestion-aware collision avoidance

The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods

Energy-efficient navigation of an autonomous swarm with adaptive consciousness

The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents. View Full-TextKeywords: autonomous swarm; multi-agent systems; energy efficient; swarm intelligence; leader follower; collision avoidance</div

Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.Comment: Accepted to IEEE SENSORS 202