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Node localization algorithms that can be easily integrated into deployedwireless sensor networks (WSNs) and which run seamlessly with proprietary lowerlayer communication protocols running on off-the-shelf modules can helpoperators of large farms and orchards avoid the difficulty, cost and/or timeinvolved with manual or satellite-based node localization techniques. Eventhough the state-of-the-art node localization algorithms can achieve low errorrates using distributed techniques such as belief propagation (BP), they arenot well suited to WSNs deployed for precision agriculture applications withlarge number of nodes, few number of landmarks and lack real time updatecapability. The algorithm proposed here is designed for applications such aspest control and irrigation in large farms and orchards where greater powerefficiency and scalability are required but location accuracy requirements areless demanding. Our algorithm uses received signal strength indicator (RSSI)values to estimate the distribution of distance between nodes then updates thelocation probability mass function (pmf) of nodes in a distributed manner. Atevery time step, the most recently communicated path loss samples and locationprior pmf received from neighbouring nodes is sufficient for nodes with unknownlocation to update their location pmf. This renders the algorithm recursive,hence results in lower computational complexity at each time step. We propose aparticular realization of the method in which only one node multicasts at eachtime step and neighbouring nodes update their location pmf conditioned on allcommunicated samples over previous time steps. This is highly compatible withrealistic WSN deployments, e.g., ZigBee which are based upon the ad hocon-demand distance vector (AODV) where nodes flood route request (RREQ) androute reply (RREP) packets in the network.

RSSI-Based Distributed Self-Localization for Wireless Sensor Networks used in Precision Agriculture