Norleaf’s R&D team has experienced researchers and engineers who collaborate with various industries and universities in developing technologies and bridging the gaps between academic research and industrial applications. Norleaf Networks is currently working on two R&D projects. Secure Routing in Adhoc Network
Ad-hoc network connectivity solutions provide an excellent alternative in emergency scenarios because they can be assembled quickly and do not require central infrastructure or much human intervention. The primary objective of ad hoc network routing is the accurate and efficient route establishment between a pair of nodes. Currently, in the standard Link State Protocols (LSP), the nodes simply flood the topology data often to ensure that the database does not remain unsynchronized. This renders the involved nodes open to malicious attacks and also makes the messaging costs too high for many applications. We research and propose solutions that involve adding a trust model that would be independent of the underlying LSP and constantly update the trust values associated with each node, thereby adding reliability in standard LSP. Since the trust model will lower the trust value of a malicious node, this node will be potentially replaced in every routing table adversely impacted, thus improving the network performance. Our work involves identifying metrics and weight assignment schemes used to calculate the node-specific trust value which will accurately characterize a given node’s behavior. Our collaborative work with Dalhousie University involves research into blockchain-based security models for wireless sensor networks

Robot-Assisted Sensor Positioning Algorithms
Emergency preparedness recognizes the need to develop wireless sensor networks to enable real-time monitoring and response to critical, random and continuous events like natural disasters. Helicopter sensor airdrop is a common method used to flood the impacted area with sensors when hazardous conditions on the ground prevent the manual placement of sensitive information monitoring equipment. Sensors are either mobile or static. Mobile sensors come equipped with the intelligence and attached hardware to self-locate to the proper location, but quickly become cost-prohibitive when deployed in large numbers. Therefore, the helicopter airdrop method must use static sensors; which often results in inadequate or redundant sensor ground placement. The uniform area coverage is achieved only by mobile robots redistributing these static sensors per the known topology of the terrain being monitored. The robot is typically limited in its battery power and its carrying capacity can vary greatly. The distance traveled by the robot is often representative of its low battery power. We research and propose energy-efficient solutions that minimize the distance that the robot equipped with a low power battery, must commute to achieve the best possible positioning over the given terrain. Tradeoffs must be carefully made with this parameter versus the carrying capacity of the robot and/or the available number of mobile robots. We are also working on efficient sensor positioning algorithms for applications where mobile sensor deployment is a more viable option (e.g. under-water sensors in lake, river or ocean bed).