Mobility issues in hybrid ad-hoc wireless sensor networks

A Hybrid Ad-Hoc Sensor Network (HANET) is presented, consisting of both mobile and stationary nodes. Wireless sensors in this domain consist of densely distributed, low-power, energy constrained stationary sensors, which are able to form MAC level connections and Network level multi-hop routes at runtime. The limited energy supplies and the interaction of sparse mobile nodes impose stringent requirements for low-complexity, low-energy, distributed protocol design.; The EAR protocol (Eavesdrop and Register) assumes a mobile-centric view of connection maintenance, as the mobile nodes are assumed to have fewer constraints on energy supplies as compared to the stationary nodes. The mobile node forms a registry based on “Eavesdropping” the signals native to the stationary network's MAC protocols. The density of the stationary network is exploited to avoid handoffs and acknowledgement messages.; We present the MIR (Mobile Intermediate Routing) protocol to handle routing issues for packets associated with mobile nodes. The stationary wireless backbone will facilitate routing, using intelligent packet forwarding and localized route updating to allow packets to be redirected en route to their destinations. Intermediate Rerouting is shown to reduce the packet delay time and the packet dropping rate.; An algorithm is presented for radio control via the MAC layer. We introduce the concept of Internal Message Updating and External Message Updating, the combination of which allows, the mobile nodes to reconfirm present connections while searching for new connections, incurring the energy costs associated with radio level signaling. We present an idea to allow mobile sensors to forgo ICU and ECU functionality by using outage prediction and taking advantage of node proximity. It is shows that this scheme can reduce energy consumption while maintaining a high quality of service.; D-PEC (Distributed Pre-Event Clustering) allows the stationary sensor network to form clusters and combine data to track and identify reoccurring targets, such as mobile nodes. This algorithm allows the network to cluster itself prior to target inclusion, thereby avoiding the costs and delays associated with on-the-fly cluster generation. A bound on the clustering signaling complexity per node is derived for the high density and low density network cases.